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Khan and Martini EURASIP Journal onWireless Communications andNetworking (2016) 2016:93 DOI 10.1186/s13638-016-0584-6

RESEARCH Open Access

QoE-driven multi-user scheduling andrate adaptation with reduced cross-layersignaling for scalable video streamingover LTE wireless systemsNabeel Khan* and Maria G. Martini

Abstract

The scarcity of the available radio spectrum coupled with the growing popularity of bandwidth intensive mobilevideo applications poses a huge challenge to network operators. The solution of over-provisioning the network is noteconomical; hence, an appropriate strategy for scheduling and resource allocation among the users in the system is ofcrucial importance. This work focuses on scheduling multiple video flows on the downlink of a wireless system basedon orthogonal frequency division multiple access (OFDMA), such as Long-Term Evolution (LTE) and LTE-A(LTE-Advanced) standards. We propose a joint multi-user scheduling and multi-user rate adaptation strategyproviding an appropriate trade-off between efficiency and fairness, while ensuring high quality of experience (QoE)for the end users. We consider Scalable Video Coding (SVC) which facilitates the truncation of bit streams, thusallowing graceful degradation of video quality in the event of wireless channel variations or network congestion. Theproposed scheduler utilizes QoE-aware priority marking, where video layers are mapped to priority classes and targetsat minimizing delay bound violations for the most important priority classes under congestion. In order to reducecongestion, we propose multi-user rate adaptation at the MAC layer via a novel dynamic filtering policy for QoE-basedpriority classes.Simulation results show that the proposed approach delivers to the end users a similar QoE as delivered by the state-of-the-art cross-layer approaches, where extensive cross-layer signaling, additional video rate adaptation modules atthe core network, and frequent link probing from the wireless access network to the rate adaptation modules arerequired. The latter approaches are not implemented in real systems due to the aforementioned drawbacks, while ourapproach can be implemented without major modifications in the standard behavior of existing networks andequipment. The proposed framework can deliver delay-sensitive traffic as well as delay-tolerant best-effort traffic.

1 IntroductionThe 4th-generation wireless technologies such as the 3rdGeneration Partnership Project (3GPP) LTE/LTE-A andthe enhanced capabilities of the recent smartphones andtablets have fostered the growth of multimedia and inter-active bandwidth demanding services, such as live videostreaming, video on demand, interactive gaming, and 2Dand 3D video streaming over wireless networks. The CiscoVisual Networking Index (VNI) projects that video con-sumption will amount to 90 % of the global consumer

*Correspondence: [email protected] of Engineering and Computing, Kingston University London, PenrhynRoad, KT1 2EE London, UK

traffic by 2019 [1]. However, supportingmultimedia appli-cations and services over wireless access, for instance,LTE base station (eNodeB) for LTE networks, is chal-lenging due to constraints such as limited bandwidth andrandom time-varying channel conditions. The eNodeB iscongested when the video traffic rate is higher than thewireless channel capacity. Scalable video, H.264/SVC [2]or Scalable High Efficiency Video Coding (SHVC) [3],is an attractive solution for real-time rate adaptation atthe wireless access network. A scalable video stream hasa base layer and several enhancement layers. As long asthe base layer is received, the receiver can decode thevideo stream. As more enhancement layers are received,

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Khan and Martini EURASIP Journal onWireless Communications and Networking (2016) 2016:93 of 23

the decoded video quality is improved. The enhancementlayers can be dropped dynamically to match the wirelesschannel capacity.While layer dropping provides a flexible way to perform

rate adaptation, it also influences the user’s perceivedvideo quality. Customer satisfaction is the main objec-tive for mobile network operators. Quality of experience(QoE) [4] reflects the user’s experience and satisfactionfor the service used. QoE evaluation can be performed viasubjective tests with the help of a panel of users, in orderto obtain a mean opinion score (MOS) [5] which reflectthe quality perceived by the observers. This reflects thefeatures of the human perceptual system, as dependenton human observation. Since subjective tests are timedemanding and costly, objective video metrics have beendeveloped to estimate the user’s perceived quality. Theseare mathematical-based metrics, ranging, for video qual-ity assessment, from mean square error (MSE) [6] andpeak signal-to-noise ratio (PSNR) [6] to structural similar-ity (SSIM) [7] and more complex metrics possibly betterestimating the perceived quality [8].Content awareness at the eNodeB requires the con-

tribution of video packets to the objective video qual-ity. Content-aware multi-user packet scheduling and rateadaptation at the eNodeB play a crucial role in determin-ing the overall customer experience. In order to provideQoE-based video delivery, two classes of strategies existin the literature. One of the solutions comprises a videoquality-aware packet scheduling and radio resource allo-cation strategy as proposed in [9–13]. The informationon the content of different video traffic flows is pro-vided through cross-layer signaling to the eNodeB. Themain goal of the strategy is to maximize the video qual-ity of the streaming users under wireless channel andbandwidth constraints. This strategy requires video pro-cessing to extract content information and/or signalingto deliver video content information at the eNodeB. Theother approach, proposed in [14–17], is the utilizationof a content-blind packet scheduler, such as proportionalfair (PF), modified largest weighted delay first (M-LWDF),or exponential proportional fair (EXP-PF), at the eNodeBwhere content-aware radio resource allocation is per-formed at a proxy located close to the eNodeB. In thisapproach, a rate adaptation module avoids congestionat the eNodeB. This approach requires additional mod-ules, proxy and rate adaptation modules, and excessivecross-layer signaling.Current LTE/LTE-A networks are not built for QoE-

aware video delivery. The LTE/LTE-A has a hierarchi-cal architecture, where video packets are first passedthrough Packet Data Network Gateway (P-GW), locatedin the core network. The P-GW transfers video packetsto the respective target eNodeB, where radio resourcesare assigned to the video packets. The P-GW has

application-specific information but it is unaware of thecongestion status at each eNodeB. On the other hand, theeNodeB is aware of the channel capacity and congestionstatus of the radio cell but it has no application-specificinformation. The authors in [18] proposed a QoE-awarevideo delivery by considering the hierarchical architectureof LTE/LTE-A network. The authors proposed a QoE-aware video packet marking at the core network andQoE-aware packet dropping at the eNodeB. The markingscheme at the core network transforms the video con-tent information into QoE-aware priority classes. There-fore, the strategy avoids complex video content processinginformation at the eNodeB. Furthermore, no rate shap-ing modules are required at the core or radio accessnetwork. However, the authors utilized a content-blindpacket scheduler. In our preliminary work [19], we stud-ied that content-aware packet scheduling plays a key rolein improving the overall QoE of the mobile users. In thiswork, we propose a content-aware scheduling and conges-tion avoidance strategy by utilizing the QoE-aware packetmarking. The main contribution of this article can besummarized as follows:

• We propose a novel QoE-based priority-awarescheduling rule. As a key difference from existingcontent-aware scheduling rules, such as in [9–13],the scheduler avoids video content processingcapabilities at the eNodeB by utilizing the QoE-awarepriority marking.

• We propose a novel QoE-based rate adaptationstrategy. The proposed strategy quantifies congestionby considering video packets sojourn time in thequeue at the eNodeB. Packet delay plays an importantrole in QoE-based video delivery. The rate adaptationpolicy proposed in [18] is delay-blind, which can havea significant impact on the QoE-based video delivery.

• We propose a joint operation of QoE-aware schedulerand rate adaptation strategy. The joint operation is incontrast to the rule in [18], where packet dropping iscontent-aware but packet scheduling is content-blind.

• The proposed scheduling strategy considers theservice needs of other traffic classes such as delay-constrained video conferencing and delay-tolerantweb browsing traffic, also referred as the best-efforttraffic. Therefore, the proposed rule is not only videoquality-aware but also traffic type-aware.

The remainder of the paper is organized as follows.Related work is discussed in Section 2. Section 3 presentsthe considered system model and problem statement.Section 4 presents the proposed packet priority sched-uler (PPS). After a description of the scheduling metricand an analysis of the factors composing it, a congestionavoidance methodology (priority class filtering policy) is


presented in Section 5. Section 6 presents the proposedscheduling rule for the best-effort traffic. The section alsodiscusses how a mixture of delay sensitive and best-efforttraffic users can be supported simultaneously. Simulationscenario and the considered benchmark strategies are pre-sented in Section 7. Finally, the proposed joint schedulingfunction and priority class filtering policy are evaluated inSection 8 along with the state-of-the-art benchmark rules.Concluding remarks appear in Section 9.

2 Related workIn the literature, several QoE-based video deliveryapproaches have been proposed. In our previous work[20], we divide these strategies into two classes, based onthe required video processing capabilities at the eNodeB,and/or additional modules requirements at the radioaccess network (RAN). These classes are briefly discussedin the following sections.

2.1 Content-aware scheduling and resource allocation atthe eNodeB

With content-aware scheduling approaches, the optimiza-tion goal is the maximization of the video quality subjectto time-varying wireless capacity. For instance in [21, 22],the concept of incrementally additive distortion is usedto determine the importance of video packets for eachuser’s precoded non-scalable video stream. Essentially, theincrease in distortion due to the loss of a video packet isa function of all the other video packets that are depen-dent on it and cannot be decoded if it is not sent. Thisinformation is used to drop video packets in the eventof congestion over the wireless interface, beginning withthe least important video packet. The authors in [13] fur-ther extended the concept of video packet importanceand proposed a joint packet scheduling and subcarrierassignment for OFDMA systems. According to [13], sub-carrier is allocated through a two-level search path. Inthe first step for each flow, the video packet contribut-ing largest to the video quality is selected. In the secondstep, the priority of each flow on a subcarrier is computedby considering the channel gain of the subcarrier and thequality contribution of the packet selected in the first step.The subcarrier is assigned to the flow having the high-est channel gain and the video packet contributing largestto the video quality. A similar approach is presented in[12] where a flow is prioritized based on the ratio of thepacket’s video quality contribution and the number of sub-carriers required to schedule the packet. The packet of aflow contributing largest to the video quality and in pos-session of the least number of subcarriers is scheduledin priority. Similar distortion-based joint packet schedul-ing and subcarrier assignment policies are presented in[9–11]. The strategies in [9–13] drop the packets vio-lating the preassigned delay bound. However, none of

the strategies consider packet deadline in the schedulingdecisions.The aforementioned content-aware scheduling strate-

gies as well as the strategies proposed in [23–28] requirevideo content information, such as distortion (if the videopacket is dropped or scheduled successfully), decodingdeadline associated with each of the video packets, decod-ing dependence of a video packet, error concealmentstrategy at the receiver, and several other parameters.These algorithms require extensive cross-layer signalingas well as video processing information at the eNodeB.However, the eNodeB manages wireless resources but itdoes not have access to complex video content informa-tion. Therefore, such scheduling algorithms pose prob-lems from an implementation point of view.

2.2 Proxy-based content-aware resource allocationThe second approach employs a QoE optimizer whichperforms radio resource allocation decision for theeNodeB. The optimizer estimates the available radioresources for video transmission by retrieving channelquality information from the eNodeB. The optimizertakes into account the content characteristics of the videostreams and performs video rate adaptation according tothe available radio resources at the eNodeB. For instance,the authors in [14, 15] proposed a QoE-based video deliv-ery by introducing two modules located inside the RAN.The two modules are the traffic engineering and trafficmanagement module. The main task of the traffic man-agement module is to act as the downlink optimizer forresource allocation, whereas the main task of the trafficengineering module is to act as a controller for perform-ing rate adaptation in the RAN. The traffic engineeringmodule performs rate adaptation either based on packetdropping or transcoding. The authors in [14] proposedthree objective functions at the optimizer. One of theobjective functions is themaximization of theMOS-basedutility, according to which the rate adaptation is doneto maximize the mean MOS (mean user-perceived qual-ity). According to the objective function, resources arefirst reserved to the users with good channel quality andlow-rate demanding applications. The authors also pro-posed amax-min fairness-based objective function, wherethe main goal of the objective function is to allocateresources such that all the users get the same perceivedquality. However, the authors do not propose any schedul-ing algorithm to be used in conjunction with the proposedcross-layer resource allocation frame work. The schedul-ing algorithm is important in determining the overallperformance of an LTE system. The work done in [16, 17]jointly addresses resource allocation and rate adaptationfor Scalable Video Coding (SVC) traffic. The authors pro-posed a proxy-based solution with limited informationexchange between the application and theMAC layer. The


main goal of the proposed framework is to maximize thesum of the achievable rates subject to the minimization ofthe distortion difference among multiple video flows.The proxy-based approach requires additional modules

at RAN and regular link probing from eNodeB to thesemodules. This approach can significantly increase thecomplexity of the network and raise the capital expendi-ture (CAPEX) for network operators.

3 Systemmodel and problem statementThe considered system model is shown in Fig. 1. We con-sider QoE-based packet marking at the P-GW, whereaspacket scheduling and rate adaptation is performed atthe eNodeB as shown in Fig. 1. The subsequent sectionsdiscuss the considered QoE evaluation approach, QoE-based packing marking strategy and the considered sys-tem model at the eNodeB.

3.1 QoE evaluationQoE is a subjective measure, and performing subjectivetests for real-time network management is not feasible.Therefore, objective QoE models have been built accord-ing to the ITU recommendations (e.g., [29, 30]). Thesemodels estimate perceptual video quality measured betterthan the traditional video quality metrics, such as MSE orPSNR. In this paper, QoE estimation is performed objec-tively by employing Video Quality Metric (VQM) [31].VQM is a full reference metric which estimates videoquality in terms of DMOS, perceptual quality differencebetween the original and the degraded video. DMOS canbe mapped to MOS which ranges from 1 (worst qual-ity) to 5 (best quality). MOS values around 3 represent anacceptable video quality with slightly annoying artifacts.The computed MOS constitutes a utility function whichis used in a marking algorithm discussed in the followingsection.

3.2 QoE-based packet marking at P-GWWe consider a video server generating a pre-encodedvideo traffic workload. Video is assumed to be encodedin different layers according to the SVC standard [2] andtemporally organized in units which can be decoded inde-pendently from each other, each referred as group ofpictures (GOP). Packet marking strategies for SVC, suchas in [32, 33], do not allow to compare and prioritize layersof multiple videos having different quality and rate charac-teristics. Therefore, we employ the content-aware packetmarking algorithm described in [34]. The packet markingstrategy in [34] allows network operators to adapt multi-ple video streams having different video layers and diversequality and rate characteristics. For instance, Fig. 2 illus-trates the basic idea of the QoE-based packet markingstrategy. According to the figure, the marking algorithmmaps scalable layers of two video streams to priorityclasses. The priority classes are identified by the priorityclass index j. Assuming 8 priority classes in the system,then packets marked with index 1, i.e., j = 1 belong tothe least important priority class: as the index increases,the priority class importance increases. The algorithmexploits the utility functions (MOS vs. bitrate) of the videostreams and marks layers according to their bitrates andcontribution towards the overall perceived video quality.The main goal of the marking is to achieve the maxi-mum overall QoE, maximization of video quality, underthe constraint of the available network resources.Apart from the SVC standard, the algorithm can mark

video packets of H.264/AVC as well as newly developedHigh Efficiency Video Coding (HEVC) standard [35] andits scalable extension. Similar to the H.264/AVC stan-dard, the temporal scalability feature is enabled in HEVCwith a hierarchical temporal prediction structure. InH.264/AVC, temporal frame rates are enhanced by addingtemporal layers. However, in HEVC, temporal sub-layers

Server

Server

SVC LayerMarking

SVC LayerMarking

P-GW

PriorityClass

Marking

Prio

rity

High

Low

OpportunisticSchedulingQoE-Curve Based

Mapping of SVC Layer toPriority Class

Fig. 1 Considered cross-layer architecture


Video 1 Video 2

Utility function1: MOS vs. Bitrate Utility function2: MOS vs. Bitrate

QoE based packet marking algorithmNumber of priority classes

Priority class index

8 6 4 2

7 5 3 1

18

High QoE Low QoE

Packets of video layers mapped to priority classes

1

Baselayer

Enhancementlayer1

Enhancementlayer2

Enhancementlayer3

Baselayer

Enhancementlayer1

Enhancementlayer2

Enhancementlayer3

Enhancementlayer4

Fig. 2 Considered QoE-based packet marking framework for SVC traffic

corresponding to different frame rates are defined withina layer. Therefore, the marking algorithm can mark eachtemporal sub-layer according to its bitrate cost and QoEcontribution. By reducing the frame rate, the transmissionbitrate is adapted. Similar to SVC, Scalable HEVC (SHVC)provides quality as well as spatial scalability. Therefore,the utilization of content-dependent utility function,MOSvs. bitrate, enables packet marking of diverse video codingstandards ranging from temporal scalable H.264/AVC tothe scalable extension of HEVC.

3.3 Systemmodel at eNodeBWe consider a single-cell scenario in which the serv-ing eNodeB is at the center of the cell. The servingeNodeB’s medium access control (MAC) scheduler con-trols all the available Physical Resource Blocks (PRBs, thebasic resource units in LTE) by allocating them to theactive flows competing for resources.Each user is assigned a queue at the eNodeB. The

packet stream for each user at the eNodeB is referredto as a flow. Packets of a flow entering the buffer at theeNodeB are stored in first in first out (FIFO) order. It isimportant to note that SVC video streaming flows haveQoE-based marked packets, identified by different prior-ity class indexes, in their respective buffers. Furthermore,the packets of delay-sensitive priority classes entering thebuffer are time stamped by the scheduler. The schedulershould assign enough resources to schedule the packetsof these classes before the delay budget. Packets violatingthe delay budget are dropped from the queue since delay-sensitive traffic has no advantage in receiving expiredpackets.

In the cell, users report their instantaneous channelquality by means of a quantized feedback called ChannelQuality Indicator (CQI). If according to the schedulingdecision more than one PRB, with different CQI values oneach PRB, are assigned to a user, then it is necessary tocalculate an average supported CQI value. The scheduleruses the Mutual Information Effective SNR Mapping [36]method to calculate a single average CQI value to be usedfor all the allocated PRBs of a particular user. The singleaverage CQI, assigned to a user, is transformed to numberof bits according to the mapping reported in [37, 38].

3.4 Problem statementIn order to mathematically formulate the problem accord-ing to the information available at the eNodeB (priorityclass index, j, of each video packet and packet delay boundDmax), we define the following parameters:H(n)i : Head-of-line packet delay (waiting time of the

packet residing in the buffer of a flow). The considera-tion of packet delay at the eNodeB is important since baseand enhancement layers need to be scheduled before theirrespective decoding deadline at the receiver.1i,j: Packet dependency indicator function for priority

class j of flow i. This assumes value 0 or 1. The indica-tor function accounts for decoding dependency of videolayers within the GOP.

σ(n)i,ϕ : An indicator whether PRB ϕ is used by flow i or

not. This assumes value 0 or 1.MPRB: Total number of PRBs available for allocation at

scheduling epoch n.The objective of the strategy is to maximize the schedul-

ing of packets, within the delay budget Dmax, with priority


j(n)i over a moving average window of size tw schedulingepochs:

max

⎛⎝ n∑

m=n−tw+1

I∑i=1

1i,j · j(m)i

⎞⎠ (1)

subject to the following constraints

σ(n)i,ϕ ∈ {0, 1}I∑

i=1σ

(n)i,ϕ = 1

(2)

H(n)i ≤ Dmax (3)

The first constraint shows that each PRB can only beassigned to one flow at scheduling epoch n. The secondconstraint implies that each video packet must be sched-uled before the delay bound Dmax, i.e., the HoL delay ofa packet must be below the prescribed delay budget. Apacket violating the preset HoL delay threshold is droppedfrom the buffer. The indicator function is 1 for packetsof priority class j of flow i if all higher priority pack-ets than class index j, over tw scheduling epoch window,are successfully scheduled. On the other hand, the indi-cator function is 0 for packets of priority class j if anyof the higher priority packets (packets marked with classindex less than j), over the moving average window tw, isdropped at the eNodeB.The problem statement implies that, for video adapta-

tion, packets are dropped from the highest enhancementlayer to the first enhancement layer due to the decod-ing dependency (within the GOP) among the layers. Thepackets of the base layer need to be scheduled withthe highest priority. Therefore, the joint scheduling andrate adaptation policy must ensure that a non-base layershould only be dropped when all of its higher enhance-ment layers are dropped.The optimal solution of the above problem has been

investigated in [12, 13], where packet importance is deter-mined through the achieved distortion when the packetis successfully scheduled. According to [12, 13], afterrelaxing the non-linear and integer constraints, the opti-mal solution at each scheduling time interval requires(ζ ·I)MPRB computations, where ζ is the number of packetsresiding in the buffer of all the flows. However, accordingto [39], scheduling metrics requiring I · MPRB computa-tions can be implemented within a scheduling epoch of1 ms (LTE’s Transmission Time Interval). Furthermore,the work in [12, 13] requires distortion-based schedulingmetric which requires extensive amount of video process-ing at the eNodeB.

4 Packet priority scheduler (PPS) fordelay-sensitive priority classes

Packet scheduling is one of the most important func-tions of radio resource management (RRM) and plays akey role in distributing radio resources among differentusers with different service needs. It determines the over-all performance of an LTE system. LTE is a multicarriersystem where radio resources are spread in time and fre-quency domains. The basic time-frequency resource unit,called a PRB, is allocated to a user every 1-ms Trans-mission Time Interval (TTI). Defining a radio resourceallocation on a per-PRB basis, as shown in Fig. 3, is sim-pler to implement and reduces complexity compared tocomplex strategies as proposed in [40, 41]. According tothe figure, the user with the highest scheduling metric isallocated a PRB. With this approach, the scheduler com-putes I · MPRB metrics per scheduling epoch. Therefore,the per-PRB scheduling rule has a linear dependence onthe number of PRBs and flows.Algorithm 1 shows the pseudo-code of the proposed

PPS rule. PRBs are assigned in the increasing order of fre-quency. According to the pseudo-code, per-PRB schedul-ing metric �

(n)i,ϕ is computed for each flow. PRB ϕ is

assigned to flow i∗ if it maximizes the scheduling met-ric �

(n)i,ϕ . The set of PRBs assigned to flow i∗, �

(n)PRB,i∗ , is

updated on each PRB allocation.

Algorithm 1 Packet priority schedulerrepeat

for ϕ = 1 toMPRB dofor i = 1 to I do

Calculate �(n)i,ϕ

end fori∗ = argmax

(�

(n)i,ϕ

)Assign PRB ϕ to flow i∗Update �

(n)PRB,i∗

end forn = n + 1

until END OF SIMULATION

The selection of the scheduling metric depends uponthe desired performance requirements of the networkoperators which can be spectral efficiency (bit/s/Hz), fair-ness (guaranteeing a minimum performance threshold),or QoS provisions (packet delivery delay bound). It isimportant to note that the main business objective ofmobile operators is the provision of multiplay applica-tions of streaming and live video, VoIP and data on asingle IP-based infrastructure. Therefore, the design ofa QoS-aware scheduling strategy becomes mandatory.According to [39], QoS-aware scheduling strategies suchas M-LWDF [42], EXP-PF [43], Log-rule [44], Exp-rules


Flow 2

Physical Resource Blocks (PRB)

freq

uenc

y

time

PRB 1

PRB 2

PRB 3

PRB 4

metric

metric

metric

3.1

1.5

2.7

Scheduler assigns PRB to user with best metric

wireless channel

Flow 1

Flow 2

Flow 3

Flow 1

Flow 3

Fig. 3 Scheduling interactive and streaming video users. The scheduling metric must consider the different service needs of both types of videoapplications

[44], and other delay-based rules proposed in [45–47]are capable of meeting end users’ flow requirements interms of packet delivery delay bounds. In our consideredframework, there are QoE-based marked priority classesfor video streaming traffic, apart from different traffictypes (real-time video and best-effort traffic). Therefore,a scheduling metric is required with a property of per-forming rate adaptation according to the importance ofpriority classes under the event of congestion. QoS-awarestrategies in [39, 42–44] lack this important property.We propose a novel scheduling metric which consid-ers the priority class index in the scheduling decisionsand schedules the most important priority classes undercongestion.

4.1 Scheduling metricThe main objective of the proposed scheduling func-tion is to minimize the probability of delay bound vio-lation of the most important packets by guaranteeingpacket delivery before the delay bound. The HoL delay isdefined as

H(n)i = n − nenteri (4)

where n is the current scheduling epoch and nenteri is thescheduling epoch when the packet of flow i enters thebuffer at the eNodeB. It is important to note that oneof the most important QoS requirements is that packetshave to be delivered within a delay bound. This constraintapplies for QoE-based video streaming priority classes as

well as for real-time applications such as video conferenc-ing and VoIP. Each application has its own delay boundas reported in the LTE QoS Class Identifier QCI [48]. Wepropose to use HoL delay normalized by the target delayfor packet delivery. The normalized HoL delay is given as

A(n)i = H(n)

iDmax

(5)

where Dmax is the delay bound of flow i’s packets. In orderto quantify the amount of congestion, we propose to usethe HoL delay of all the flows in the system. The amount ofcongestion in the system is quantified by the normalizedHoL delay averaged over the I delay-sensitive flows in thesystem and given as

A(n) = 1I

I∑i=1

A(n)i . (6)

The parameter A(n) quantifies the system load: whenA(n) is equal to 0.5, it means that on average every flow’spacket is experiencing an HoL delay of 50 % the delaybound. Due to diversity in the channel quality and datarate of the applications, some flows’ packets may be expe-riencing a higher HoL and some less.By considering the QoS constraints of different traffic

types and QoE-based priority classes, we propose to usethe following per-PRB scheduling metric:


�(n)i,ϕ = W (n)

ϕi

[χ

(n)i,ϕ

R(n)i,ave

]W (n)

qi

[N (n)qi

](7)

The scheduling metric depends upon the followingparameters:

• N (n)qi is the number of packets currently residing in the

queue of flow i at scheduling epoch n. Consideringthe queue status in the scheduling decision avoidsbuffer overflow and keeps the queues stable.

• W (n)qi is the weight of the HoL packet.

Mathematically, it is defined as:

W (n)qi = exp

{[j(n)i

]A(n)

A(n)i

}(8)

where j(n)i is the priority class index of the HoL

packet of flow i. It is important to note that ourproposed weight design depends on the system load.The higher the system load, the higher will be thenormalized system delay A(n), which results in ahigher weight for the packets of the most importantpriority classes. If the system delay is low, packetsfrom different priority classes have approximately the

same weights. Therefore, the delay-based priorityweight makes the scheduling rule dynamic. Theimpact of the exponential weight at different systemloads (normalized system delay) and HoL delays isshown in Fig. 4. According to the figure, packets ofpriority classes 13, 7, and 1 have approximately thesame weights when the normalized system delay is0.25. Under congestion (normalized system delay of0.75), the weight of the most important priority classincreases exponentially, w.r.t. the lower priorityclasses, with the increase in packet’s waiting time inthe queue.

• χ(n)i,ϕ is the channel quality, in terms of bit/s/Hz, of

PRB ϕ. It is important to note that effectiveutilization of the radio resources is extremelyimportant. We utilize the CQI feedback from user iin determining the channel quality of PRB ϕ. Thisfactor makes the scheduling rule channel aware andincreases the system efficiency in terms of bit/s/Hz.

• R(n)i,ave is the time-averaged throughput.

Mathematically, it is defined as

R(n)i,ave = R(n−1)

i,ave

(1 − 1

nw

)+ 1

nwR(n−1)i (9)

where R(n−1)i,ave is the average throughput at scheduling

instant n− 1. R(n−1)i is the number of bits transmitted

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

5

10

HoL packet’s waiting time (Normalized w.r.t target delay)

Pri

ori

ty c

lass

wei

gh

t Lower system load (0.25 normalized system delay)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

20

40


Pri

ori

ty c

lass

wei

gh

t Moderate system load (0.5 normalized system delay)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000


Pri

ori

ty c

lass

wei

gh

t High system load (0.75 normalized system delay)

Priority Class 13Priority Class 7Priority Class 1



Fig. 4 Priority weight of the HoL packets (priority classes 13, 7, and 1) under normalized system delay of 0.25, 0.5, and 0.75


at scheduling instant n − 1. nw is the size of the time-average window. This term represents the achievedpast average throughput by user i at schedulinginstant n and is updated at every TTI. This providesproportional fairness in the scheduling decisions. Theuser experiencing the lower time-average throughputwill be prioritized based on its channel conditions.

• W (n)ϕi is the weight of the PRB.

W (n)ϕi = χ

(n)i,ϕ

χ(n)i

(10)

where

χ(n)i = 1

MPRB

MPRB∑ϕ=1

χ(n)i,ϕ . (11)

χ(n)i is the average PRB spectral efficiency of user i at

scheduling instant n.W (n)ϕi gives information on the

variable amount of fading on the PRBs of each user.For instance, the reader can refer to Fig. 3. When thescheduler calculates the scheduling metric for PRB 2,the channel quality of the other 3 PRBs (PRB 1, PRB3, and PRB 4) of the user is not considered. However,withW (n)

ϕi , the channel quality of all the PRBs isconsidered in the scheduling metric. If a user isexperiencing a high interference on some of the PRBsand other PRBs have better channel quality, then thisfactor assigns a lower weight to the PRBs with poorchannel quality. On the other hand, the PRBs withthe best channel quality for a user will be assigned ahigher weight, thus utilizing the independentmulti-user frequency selective fading.

At moderate normalized system delay, the scheduleracts like a queue-aware proportional fair scheduler, as thepriority class weights are approximately the same. There-fore, the channel quality of a flow has a higher impactin the scheduling decisions. At higher normalized sys-tem delay, the weight function increases exponentially forthe most important priority classes. Therefore, the sched-uler minimizes the delay bound violations of packets fromthe most important priority classes. Under congestion,the scheduler acts like a strict priority scheduler with lessimportance of channel quality and more importance ofhigher priority class packets in the scheduling decisions.The next section discusses a novel congestion avoid-

ance methodology at the MAC layer which avoids theoverloading of the scheduling function.

5 Congestion avoidance through dynamicpriority class filtering

QoE-based SVC layer priority marking prioritizes multi-ple video streams based on their QoE contribution. Rate

adaptation on QoE-based priority classes has the poten-tial of achieving optimal radio resource utilization. Unlikestate-of-the-art scheduling strategies, the scheduler pro-posed in Section 4 exploits packet marking and rate adap-tation (under congestion) is performed by prioritizing theimportant priority classes and reducing the resource allo-cation probability of the less important priority classes.However, rate adaptation solely relying on the schedulingfunction presents the following issues:

• Under congestion, the less important packets residingin the buffer till the delay bound block packets ofimportant priority classes. This phenomenon is alsoknown as HoL blocking. When the HoL packetsbelong to the least important priority class then,under congestion, higher priority packets have towait to be scheduled till the least important priorityclass packets are dropped from the buffer.

• At high system delay, packets belonging to lowimportance priority classes residing in the buffer ateNodeB are dropped when their delay bound isreached. Lower priority class packets, residing in thebuffer till the delay bound, increase the averagesystem delay, which in turn increases the resourceallocation probability of the most important priorityclasses. The system becomes strictly priority drivenwhen the normalized average system delay is high,i.e., high system delay reduces the channel awarenessof the scheduler: highest priority packets are assignedresources with reduced importance of channelquality in the scheduling decisions. This leads to areduction in the system efficiency (bit/s/Hz).

• The resource allocation probability of less importantpriority classes depends upon the system load(normalized system delay). However, due to theprobabilistic arrival of the incoming traffic andstochastic nature of the wireless channel, theresource allocation probability of less importantpriority classes changes randomly. This leads tofluctuation in the perceived video quality of the usersdue to variation in the system load and wirelesschannel capacity. According to [49], multimediaservices users would usually prefer to keep a fairlyconstant quality level rather than being exposed tofluctuations in the video quality.

In light of the aforementioned issues, we propose anovel concept of multi-user rate adaptation by apply-ing a filtering policy on QoE-based priority classes ofSVC flows. The joint operation of the scheduler andmulti-user rate adaptation is shown in Fig. 5. The pri-ority class filter utilizes the metrics calculated by thescheduling function and blocks the least important pri-ority classes from entering the buffers at the eNodeB.Under congestion, the PPS scheduling function reduces


eNodeB

System information over lastscheduling epochs:• normalized system delay• throughput and channel

quality offlows• system packet loss rate of

the least importantpriority class

Lowest Priority Class

Flow 1 Flow 2 Flow 3 Flow 4

Priority Class Filter(limits overload of scheduler andavoids

HoL blocking by filtering SVC layerswith low QoE contribution)

Scheduler(efficientradioutilizationandlow loss

for highpriority packets)Highest Priority Class

Fig. 5 Interaction between the scheduler and priority class filter

the resource allocation probability of the least impor-tant priority classes. The result is a delay bound viola-tion of lower priority packets. The priority class filteringalgorithm exploits these characteristics of the schedulingfunction and reduces congestion and HoL blocking. Fur-thermore, it also reduces fluctuations and provides fairlyconstant perceived video quality. The details of the prior-ity class filtering algorithm are provided in the followingsection.

5.1 Hysteresis-based policy for rate adaptation throughdynamic priority class filtering

In order to quantify the congestion, the PPS schedulingfunction calculates the normalized average system delayA(n) at each scheduling epoch. We propose to utilizethis congestion information in the priority class filteringdecisions. Figure 6 shows the basic concept of the pro-posed policy. According to the figure, there is an upperand lower limit of the normalized average system delay.The proposed window-based filter policy is based ondual threshold action. The decision whether a flow’s pri-ority class packet is allowed or blocked from enteringthe buffer is taken every tw scheduling epochs. Restrict-ing the admission of priority class packets under highersystem delay (point P1 in Fig. 6) will decrease the aver-age system delay and increase the scheduler’s channelawareness, resulting in better exploitation of multi-userchannel diversity as shown in point P2. In order to reducethe under utilization of radio resources, the re-admissionof priority classes is very important as shown in pointP3. Therefore, the main goal of the filtering policy isto maintain the average system delay between the twothresholds.

We propose to perform rate adaptation by droppingpackets marked with the lowest priority class index (lessimportant priority class in terms of user satisfaction). Letj∗ be the lowest priority class index, θ(i, j∗) be the set offlows having packets of priority class j∗, and δ(i, j∗) be theadmission control vector containing the IDs of the blockedflows for priority class j∗. Flows’ priority class j∗ pack-ets are blocked from entering their respective buffers iftheir IDs are removed from θ(i, j∗) and added to δ(i, j∗).When flows’ priority class j∗ packets are re-admitted, thentheir IDs are transferred back from δ(i, j∗) to θ(i, j∗). Thefiltering process comprises a two-step policy. Step onecomputes the number of flows to block or re-admit for theleast important priority class j∗, whereas step two iden-tifies the flows whose priority class j∗ is blocked fromentering the buffer at eNodeB.

5.1.1 Step oneAccording to the PPS scheduling metric, the priorityweight decreases the resource allocation probability ofthe lowest importance priority class when the normalizedinstantaneous system delay is high. In order to facilitatethe priority class filter decisions, we calculate the num-ber of transmitted packets of the current lowest priorityclass represented by index j∗ and the number of pack-ets dropped due to delay bound violation. Let n′ be thescheduling epoch when a priority class filtering decisionis taken. The system packet loss ratio, over tw schedulingepochs, at n′ is:

plr(n′)

j∗ =∑n′

m=n′−tw+1 P(m)drop∑n′

m=n′−tw+1

(P(m)

transmit∗j+ P(m)

drop

) (12)


Fig. 6 Hysteresis principle for dynamic priority class filtering

whereP(m)

transmit∗j: Number of transmitted packets of class j∗ over

the moving average transmission window tw.P(m)drop: Number of dropped packets over the moving

average transmission window tw.The scheduling metric computes the normalized sys-

tem delay, A(n), which quantifies the congestion status ofthe network. We propose to utilize the normalized systemdelay averaged over tw scheduling epochs:

H(n′) = 1

tw

n′∑m=n′−tw+1

(A(m)

)(13)

whereH(n′)

indicates congestion in the network by calcu-lating the average of the normalized system delay over themoving average transmission window of size tw epochs.Flows are blocked or re-admitted according to the follow-ing rules:

Nblockj∗ =⌊Ij∗ · H

(n′)

· Shyst · plr(n′)

j∗

⌋(14)

Nre−admitj∗ =⌊Ij∗ ·

(1 − H

(n′))

· (1 − Shyst)⌋

(15)

where Nblockj∗ is the number of flows blocked for class j∗and Nre−admitj∗ is the number of flows re-admitted. Thenumber of flows to block or re-admit is taken once everytw scheduling epochs. Shyst is the output of the hysteresis-based window process. Ij∗ denotes the total number offlows for priority class j∗. The higher the congestion, the

higher the delay bound violations which in turn results ina higher number of flows blocked for priority class j∗. Sim-ilarly, the lower the normalized system delay, the higherthe number of re-admitted flows. The hysteresis-basedwindow output Shyst adds stability in the blocking and re-admission decisions. It is based on the principle of dualthreshold, which states that when the input is higher thana certain chosen threshold, the output is high. When theinput is below a different (lower) chosen threshold, theoutput is low; and when the input is between the two lev-els, the output retains its last value. Mathematically, it isdefined as:

S(n′)

hyst =

⎧⎪⎪⎪⎪⎨⎪⎪⎪⎪⎩P(Sthrh

), if H

(n′)

> Sthrh

S(n′−tw

)hyst , if Sthrl ≤ H

(n′)

≤ Sthrh

ρ

(n′)

, if H(n′)

< Sthrl

(16)

where

ρ(n′) = S(n′−tw)

hyst · (1 − ω) + ω · P(Sthrl) (17)

Sthrl and Sthrh are the lower and higher threshold limits

of the averaged normalized system delay, H(n′)

, respec-tively. Similarly P(Sthrh) and P(Sthrl) are the probability ofdelay bound violation based on the congestion status ofthe network. The higher the congestion, the higher theprobability of delay bound violation. Intuitively, the higherthreshold limit Sthrh of the normalized system delay is setto a point where the probability P(Sthrh) of delay boundviolation is 1. Similarly, the lower threshold limit is set toa point where the probability of delay bound violation is


very low. ρ

(n′)

is a factor which determines urgency inthe re-admission decisions, ω is a constant (less than 1)

used in the exponential moving average weight, ρ(n′)

. Thespeed at which the hysteresis output decreases, every twcycle, depends up on ω. The higher the ω (close to 1), thehigher the re-admission speed (under low system delay)for priority class j∗ flows.The basic operation of the hysteresis based priority class

filter is shown in Fig. 7. According to the figure, the output

S(n′)

hyst latches to P(Sthrh) when the congestion parameter

H(n′)

crosses the higher threshold limit. The hysteresis

output is latched to P(Sthrh) when H(n′)

is in between thetwo thresholds, i.e., the hysteresis output is retained to its

last value S(n′−tw

)hyst . When the congestion parameter H

(n′)

reaches the lower threshold limit as shown by the pointP1, the output Shyst changes to ρ which is the exponen-tial moving average equation given in (17). The outputdecreases, after every tw epochs, according to the movingaverage Eq. (17) shown by points P2 and P3. The hys-

teresis output is retained, S(n′−tw

)hyst , when the congestion

parameter H(n′)

crossovers the lower threshold limit asshown by point P4. The hysteresis output remains latched

untilH(n′)

crosses either the lower or the higher thresholdlimit.

5.1.2 Step twoAfter computing the number of flows to block for prior-ity class j∗, the next step is to determine the flows whosepriority class j∗ is blocked from entering the buffer ateNodeB. We propose to block the flows having the low-est ratio of channel quality to time-averaged throughput.In order to compute this, we utilize the average PRB spec-

tral efficiency χ

(n′)

i of user i computed by the schedulingfunction in (11). After tw scheduling epochs, the time-

averaged channel quality χ

(n′)

i is given as

χ

(n′)

i = 1χmax

⎡⎣ 1tw

n∑k=n′−tw+1

χ(k)i

⎤⎦ . (18)

Furthermore, we also utilize the time-averaged through-put R(n)

i,ave computed by the scheduling function in (9). The

metric α(n′

)i determines which of the flows have to be

blocked or re-admitted and is given as

α

(n′)

i = χ

(n′)

i

R(n)i,ave

. (19)

5.1.3 The pseudo-code of hysteresis based priority classfilter policy

The pseudo-code of the priority class filtering process isshown in Algorithm 2. According to the algorithm, the

Fig. 7 Basic operation of hysteresis based priority class filter policy


Algorithm 2 Hysteresis based priority class filter policySet Simulation_timerepeat

form = n tom = n′ doCalculate P(m)

transmit∗jand P(m)

drop at each scheduling epochend forCalculate plr(n

′)

j∗ , for class j∗, according to (12)

Calculate H(n′) according to (13)

if plr(n′)

j∗ > 0 thenFor class j∗ calculate the number of flows to block, Nblock∗

j, according to (14)

if Nblock∗j

> 0 thenUpdate δ(i, j∗) and θ(i, j∗) by computing θasc(i, j∗)if |θ(i, j∗)| == 0 then

j∗ = j∗ + 1end if

end ifelse

For class j∗ calculate the number of flows to re-admit, Nre−admit∗j , according to (15)

if Nre−admit∗j > 0 and Hn′< Sthrl then

Update δ(i, j∗) and θ(i, j∗) by computing δdesc(i, j∗)if |δ(i, j∗)| == 0 then

j∗ = j∗ − 1end if

end ifend if

until END OF Simulation_time

number of transmitted packets of the current lowest pri-ority class j∗ and the number of packets dropped due todelay bound violations are calculated at each schedulingepoch. After tw scheduling epochs, the packet loss ratio

plr(n′)

j∗ and congestion parameter H(n′)

are computedaccording to (12) and (13), respectively. The filter blocksor re-admits flows based on the following conditions:

• If Nblock∗j

> 0: The filter transforms the setof flows for priority class j∗, θ(i, j∗), to θasc(i, j∗).θasc(i, j∗) contains the set of flows sorted in anascending order of α(n′

)i . During the priority class

filter decision epoch, the first Nblock∗jIDs are

removed from θasc(i, j∗) and added to δ(i, j∗). Ifpriority class j∗ for all the flows is blocked, then|θ(i, j∗)|, cardinality of θ(i, j∗), is 0. If this condition ismet, then, in the next filter cycle, packets of priorityclass j∗ + 1 are blocked based on the congestion andpacket loss ratio. It is important to note that if thecurrent lowest priority class is j∗, then all flow packetsmarked with index less then j∗ are blocked fromentering the buffer.

• If Nre−admit∗j > 0 and H(n′) < Sthrl : The admission

control row vector δ(i, j∗) is transformed toδdesc(i, j∗). δdesc(i, j∗) contains the set of flows sortedin a descending order of α(n′

)i . If H(n′

) is below Sthrl ,then Shyst will decrease exponentially, according to(17), with each tw cycle. Shyst will continue todecrease until H(n′

) ≥ Sthrl . After tw schedulingepochs if Nre−admit∗j > 0, then δ(i, j∗) is transformedto δdesc(i, j∗). First, Nre−admit∗j number of flows’ IDsare removed from δdesc(i, j∗) and added to θ(i, j∗). Ifall the flows for priority class j∗ are re-admitted thenafter tw epochs, flows for priority class j∗ − 1 will bere-admitted based on Nre−admit∗j .

5.2 Computational complexityIn this section, we analyze the computational complex-ity of the proposed strategy. Let I refer to the totalnumber of flows in the system and MPRB refer to thetotal number of PRBs in the system. The worst casecomputation complexity appears at the priority classfilter decision epoch n′ which consists of schedulingand priority class filtering policies. The scheduling rule


computes I · MPRB metrics and thus has a complexity ofO(I · MPRB).The metrics such as normalized system delay A(n),

time-averaged throughput R(n)i,ave, and channel quality

χ(n)i are computed by the scheduler at each schedul-

ing epoch. Therefore, the processing burden introducedby the filter is minimal. The priority class filter-ing decision is performed once every tw schedulingepochs. The first step computes the number of flowsto block or re-admit. This step computes only onemetric, either (14) or (15). The computation complex-ity for this step, O(1), is independent of the numberof flows and priority classes. The second step iden-tifies the flows whose least important priority classis blocked or re-admitted. This step computes α foreach of the flows. Furthermore, the admission con-trol vector is sorted according to α. Therefore, thecomputation of α and sorting requires O(I + I. log(I))operations.

6 Scheduling rule for the best-effort traffic classDelay-tolerant traffic (web browsing or data transferapplications such as FTP) is regarded as best-effort traf-fic class. QoS constraints, in terms of packet delay, ofbest-effort traffic class are not as stringent as that ofvideo conferencing and video streaming applications. Itis important to note that best-effort traffic correspondsto a significant proportion of mobile’s traffic. It has beenreported in [1] that by 2019, 72 % of total mobile’s traffic

will be video, followed by the Web/Data traffic contribut-ing to 19 % of total mobile traffic. Therefore, delay-tolerant traffic needs to be prioritized carefully and mustbe part of an overall traffic shaping scheme. In order toconsider delay-sensitive and delay-tolerant traffic classessimultaneously, the following criterion must be met:

• Delay-sensitive flows must meet their desired QoS,and delay-tolerant flows must receive the maximumpossible throughput without compromising the QoSconstraints of the delay-sensitive flows.

In the literature [39], composite scheduling rules servebest-effort traffic by using the classical proportional fairrule, i.e., ratio of instantaneous channel quality to thetime-averaged throughput. They prioritize delay-sensitivetraffic by considering either the logarithmic, exponen-tial, or linear function of the HoL delay. In this work,we propose to dynamically prioritize best-effort trafficby utilizing the normalized system delay. We designa dynamic weight for the best-effort traffic which isW (n)

best−effort = C1−A(n) with C > 1, where A(n) is thenormalized system delay of the delay-sensitive flows andC is a prioritization factor for the best-effort traffic. Thehigher the prioritization factor, the higher the priorityweight for the best-effort traffic under lower normalizedsystem delay. The prioritization weight at different systemdelays and with different values of C is shown in Fig. 8.The composite scheduling rule for both the traffic types is

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11

10

20

30

40

50

60

70

80

90

100

Normalized system delay

Wei

gh

t o

f th

e b

est−

effo

rt t

raff

ic c

lass

C = 25

C = 50

C = 75

C = 100

Fig. 8 Priority weight of the best-effort traffic class


�(n)i,ϕ =

⎧⎪⎪⎨⎪⎪⎩

W (n)ϕi

[χ

(n)i,ϕ

R(n)i,ave

]W (n)

qi

[N (n)qi

], if j ∈ delay-sensitive traffic classes

W (n)best−effort

[χ

(n)i,ϕ

R(n)i,ave

]if j ∈ best-effort traffic class

(20)

Delay-sensitive traffic classes (QoE-based classes forSVC traffic, video conferencing, and VoIP classes) are pri-oritized by the scheduling rule proposed in Section 4,whereas the priority of the best-effort traffic dependsupon the normalized system delay of delay-sensitive flows.At higher normalized system delay, the scheduling rule forthe best-effort traffic reduces to a simple proportional fairrule as the dynamic weight reduces to 1 as shown in Fig. 8.The aforementioned composite scheduling rules alongwith the hysteresis-based filter on QoE-based priorityclasses have three important properties:

• Higher normalized system delay: The delay-sensitivetraffic classes are prioritized by the consideration ofthe exponential weightW (n)

qi and the queue size N (n)qi

in the scheduling decisions. Therefore, QoSconstraints of the delay-sensitive traffic are alwaysmet under congestion. Under congestion, the backlog(packets waiting to get scheduled) of the best-efforttraffic increases. When the system is heavilycongested with delay-sensitive traffic, the filter blocksbandwidth demanding video traffic’s lower priorityclasses which reduces the normalized system delay.

• Moderate normalized system delay: If the normalizedsystem delay is between the two thresholds, thepriority of the best-effort traffic class depends uponthe number of packets residing in the buffers of delay-sensitive flows. The higher the number of packetsresiding in the buffer, the higher the probability ofcongestion which results in a higher resourceallocation probability of the delay-sensitive flows.

If the queue size of delay-sensitive flows is such thattheir input traffic rate is in the achievable rate region(the queue size remains stable), then the resourceallocation probability of best-effort traffic increases.Under such condition, best-effort traffic flows getscheduled subject to the condition that the queue sizeof the delay-sensitive flows remains stable.

• Lower normalized system delay: Under suchcondition, the resource allocation probability of thebest-effort traffic class is maximum as shown by theproposed weight design in Fig. 8. The result is areduction in the backlog of the best-effort traffic, thusfully exploiting the variable bitrate characteristics ofthe video traffic as well as the probabilistic arrival ofthe incoming traffic.

In the subsequent sections, we analyze the performanceof the QoE-based joint scheduling and priority class filter-ing strategy.

7 Simulation setup7.1 Simulation scenario 1In order to investigate the performance of the proposedjoint scheduling and priority class filtering algorithm,an LTE link-level simulator [37, 38] built on MATLAB’sobject-oriented features is selected as the simulation plat-form. The video sequences are encoded with the SVCcodec (medium grain scalability (MGS) [2]) and com-prise a base layer and 12 quality layers. MGS scalabilityprovides sufficient bitrate granularity for rate adapta-tion. The increase in MOS score, computed by VQM toMOS mapping as reported in Section 3.1, along with theaddition of each quality layer is shown in Fig. 9. Thewireless simulation parameters are reported in Table 1.According to the simulation parameters, the average sys-tem capacity is approximately 6.8 Mbps (2.266 bit/s/Hz

0 200 400 600 800 1000 12001.5

2

2.5

3

3.5

4

4.5

5

Bitrate (kbps)

QU

AL

ITY

(M

OS

)

ICENEWSSOCCERCREW

Fig. 9MOS vs. bitrate characteristics for each of the considered videos sequences with the addition of each SVC quality layer


Table 1 Simulation parameters—downlink LTE scheduling formulti-class traffic

Parameters Value

Bandwidth, carrier frequency 3 MHz, 2.1 GHz

UE distribution, cell radius Uniform, 1 km

Channel 3GPP-TU (typical urban)

Pathloss model Hata-Cost-231 model

Shadowing model Log-normal shadow fading

HARQ Up to 3 synchronous retransmissions

Channel fading Block fading (1 ms)

UE speed 15 to 100 km/h (users movingindependently at variable speed)

Video resolution CIF (352 · 288)Video frame rate 30 FPS

Encoder JSVM (9.15)

considering a 3-MHz bandwidth). Our main goal is toanalyze the performance of the proposed strategy undercongestion. Therefore, we simulate a loaded network with4 Ice, 4 News, 4 Soccer, and 4 Crew video streamingusers corresponding to an input average traffic rate of14 Mbps (4.66 bit/s/Hz). In addition, we simulate 4 high-priority video conferencing users, each having averageinput traffic rate of 200 kbps. The combined input traf-fic rate is 14.8 Mbps (4.9333 bit/s/Hz) against the inputcapacity of 2.266 bit/s/Hz. Thus, the average input trafficrate is approximately more than twice the system capac-ity. The packet delivery target delay of interactive videoflows is 150ms, whereas for video streaming flows the tar-get delay is 400 ms. These QoS parameters are selectedaccording to the LTE QCI [48]. We propose to use thefollowing strategies:

• Simplified fine granularity (SFG) [13] schedulingbased on packet’s contribution towards video quality.This strategy is similar to the ones proposed in[9–12]. The algorithm comprises a two step process.At each scheduling epoch, the scheduler sorts packetsof a flow based on the ratio of packet’s contributiontowards video quality and its size. The priority of aflow on each PRB is computed by the product ofchannel quality and the ratio computed in theprevious step. PRB is allocated to the flow maximizingthe priority function. A detailed implementation ofthe SFG scheduling rule is given in [13].

• Proxy-based rate adaptation [50]: We assume that aproxy is located close to the eNodeB. The proxyresponds quickly to the dynamic wireless channel andcongestion by performing rate adaptation. In order toperform rate adaptation, the proxy considers the rate-quality trade-off model, Fig. 9, of the video streamingsequences, the channel quality, and the buffer statusof all the video streaming flows. The main goal of theproxy is to maximize the sumMOS associated withdifferent SVC streams based on the periodiccongestion signal from the eNodeB. The eNodeButilizes a QoS-aware M-LWDF packet scheduler atthe eNodeB which considers the target packetdelivery needs of different traffic types. A detailedimplementation of the M-LWDF is given in [39].

• Proposed framework: QoE-based packet marking isperformed at the core network by utilizing thecontent-dependent utility functions (quality vs.bitrate function for each of the considered videosequences) shown in Fig. 9. The mapping of SVClayers into priority classes is shown in Fig. 10. Thetotal number of priority classes is 13. According tothe figure, 13 video layers (1 base and 12 qualitylayers) are mapped into 12 priority classes. For

1 2 3 4 5 6 7 8 9 10 11 12 13

ICE 1 ICE 3

ICE 4ICE 2

NEWS 1

SOCCER 2

SOCCER 1

NEWS 2

CREW 1

CREW 2

NEWS 3

NEWS 4

SOCCER 3

SOCCER 4

CREW3

CREW4

12 11 8 8 5 5 5 4 3 3 1 1 1

12 10 8 8 5 5 4 4 2 2 1 1 1

12 10 8 8 7 7 6 6 3 3 3 3 1

12 10 8 8 6 6 6 6 3 3 3 3 1

12 9 7 7 7 7 4 4 2 2 1 1 1

11 9 7 7 7 7 4 4 2 2 1 1 1

9 9 8 8 6 6 5 5 4 4 2 2 1

9 8 8 8 6 6 5 5 3 3 2 1 1

12 11 8 8 5 5 5 5 3 3 1 1 1

12 10 8 8 5 5 4 4 3 2 1 1 1

12 10 8 8 7 7 6 6 3 3 3 3 1

12 10 8 8 7 6 6 6 3 3 3 3 1

12 9 8 8 7 7 4 4 2 2 1 1 1

11 9 7 7 7 7 4 4 2 2 1 1 1

10 9 8 8 6 6 5 5 4 4 2 2 1

9 9 8 8 6 6 5 5 4 3 2 2 1

1 2 3 4 5 6 7 8 9 10 11 12 13

Fig. 10 QoE-aware priority layer marking for 16 H.264/SVC video streams. SVC layers are marked based on their contribution to the overall QoEaccording to the greedy search algorithm in [34]


instance if we consider Ice 1 video flow, the base layeris assigned priority class 12 and the first qualitysub-layer (SVC layer 2) is assigned priority class 11.Similarly, the last 3 quality layers (SVC layers 11, 12,and 13) are assigned priority class 1. The videoconferencing flows are assigned the most importantpriority class, i.e., class index 13 whereas priorityclasses 1 to 12 are assigned to the SVC layersaccording to the mapping shown in Fig. 10.Rate adaptation is performed via the joint operationof scheduler and priority class filter. Hysteresis-basedpriority class filtering decisions are taken every 250TTIs (tw = 250ms, 4 rate adaptation decision pointsper second). Furthermore, the higher limit of thethreshold Sthrh is set to 0.6. Once the averagenormalized system delay crosses 0.6 (averaged over250 TTIs), the probability of delay bound violationP(Sthrh) is maximum, i.e., 1. On the other hand, thelower limit Sthrl is set to 0.2 (averaged over 250 TTIs)with lower limit probability of delay bound violationP(Sthrl) set to 0.1. Re-admission speed is set to 0.02,i.e., ω = 0.02.

• QoE-aware packet dropping [18]: In this strategy,QoE-aware packet marking is performed at theP-GW. The marking information is utilized at theeNodeB by dropping low-priority packets under theevent of eNodeB congestion. The packet droppingalgorithm drops the QoE-marked priority packetsbefore they are fed into the scheduler. The functionof the packet dropping algorithm is to shape thetraffic according to the wireless transmissioncapacity, whereas the function of the scheduler is toperform packet scheduling onto the radio resources.It is important to note the scheduler is not aware ofthe QoE marking. Therefore, we utilize a packetdelay-aware M-LWDF scheduling rule.

• No rate adaptation: We assume that there is noadmission control policy and the system runs underoverloaded condition. We selected a link-efficientBest-CQI scheduler which assigns PRBs based on thechannel quality. In addition to the rate maximizingscheduling strategy, we also consider a QoS-basedM-LWDF scheduling strategy. According to [51],M-LWDF is the best scheduling rule fordelay-sensitive applications in terms of fairness andefficiency.

7.2 Simulation scenario 2In order to evaluate the QoS/QoE performance of thedelay-sensitive traffic under the presence of best-efforttraffic, 4 full buffer best-effort flows are added to thenetwork in addition to the 16 video streaming and 4video conferencing flows of the previous scenario. Afull buffer source is greedy in nature having an infinite

number of packets in the buffer. Therefore, we aim to ana-lyze the throughput performance of the best-effort trafficunder the presence of bandwidth-intensive and delay-constrained video applications by using the proposedcomposite scheduling rule in Section 6. We compare theproposed rule with the logarithmic and the exponentialscheduling rules, proposed in [44], at the eNodeB withproxy-based rate adaptation. A detailed working of thesemultli-class scheduling algorithms is given in [39, 44].These rules schedule the best-effort traffic by using theclassical proportional fair rule and prioritize the delay-sensitive traffic by the logarithmic (LOG-RULE) and theexponential (EXP-RULE) function of the HoL delay.The simulation results of the aforementioned strategies

are analyzed in the following section.

8 ResultsThe subsequent sections report the performance of theproposed scheme in comparison to the state-of-the-artapproaches, in different scenarios.

8.1 Joint scheduling and priority class filter performanceunder scenario 1

Figure 11 shows the average system delay when all thevideo streaming and video conferencing flows enter thesystem and the wireless access system becomes highlycongested. The figure also shows the percentage of flowsblocked for the priority classes over the high load simula-tion period. According to the figure, there is a significantincrease in the system load after 5 s. Under congestion, thenormalized average system delay decreases the resourceallocation probability of the least important priority class(priority class 1). This leads to an increase in the delaybound violations of the least important priority class. Thenormalized system delay along with the packet loss ratiois calculated over the moving average window. These twoparameters are utilized in the priority class blocking of thevideo flows.After 5.4 s, the normalized averaged system delay

increases above the upper threshold of the hysteresis win-dow with very high system PLR in priority class 1 whichtriggers the blocking of the flows. After blocking priorityclass 1, the reduction in the normalized system delay isminimal which does not decrease the delay bound viola-tion of the current least important priority class. Accord-ing to the figure, all the flows of priority class 2 are blockedin the subsequent priority class filter cycles. After block-ing of priority classes 1 and 2, there is no reduction inthe normalized system delay and PLR which is an indica-tion that further rate adaptation is required. In the nextfilter cycle, 55.55 % of the flows of priority class 3 areblocked. According to Fig. 10, priority class 3 has 9 videoflows. Therefore, 5 of the 9 video flows are blocked forthis class. The priority class filter exploits the proportional


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Fig. 11 Priority class blocking based on the hysteresis-based filter algorithm

fair rule and blocks 5 flows having lower channel qualityand higher throughput as shown in Fig. 11. After blockingthese flows, the average system delay remains between thetwo thresholds till point “P1” (9.25 s). This is an indica-tion that the input arrival rate is within the achievable rateregion according to the delay bound constraints.The average system delay crosses the lower threshold

at 9.25 s. The hysteresis output decreases exponentially,according to (15), with each window cycle having systemdelay below the lower threshold. The lower the hysteresisoutput, the higher the re-admission probability. The filterre-admits the ICE 3 and News 3 flows which increases the

average system delay, thus inhibiting the re-admission offurther flows for priority class 3. It is important to note atpoint “P2” that the system delay crosses the lower thresh-old for approximately 0.5 s. The decrease in the hysteresisoutput is not sufficient enough to re-admit flows. Thehysteresis-based process adds stability and decreases thevariations in perceivable video quality due to variations inthe input arrival traffic and wireless link capacity.The achieved MOS and the associated channel qual-

ity for each of the video users are shown in Fig. 12.According to the figure, the proxy-based strategy per-forms best, in terms of the achievedMOS, followed by the

Ice1 Ice2 Ice3 Ice40

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Fig. 12MOS performance for each of the video users under scenario 1


proposed joint scheduling and filter policy relying on QoEmarking.It is important to note that the system schedules priority

classes 4 to 13 of all the flows with zero PLR. The PLR, dueto scheduling (delay bound violation) and priority class fil-tering, of SVC layers is shown in Fig. 13. According to thefigure, all the soccer flows receive the same number of SVClayers. This is mainly due to the fact that the filter blockspriority classes 11 and 12 for all the video flows as shownin Fig. 11. Therefore, 5 enhancement layers (SVC IDs 9 to13) are blocked by the filter for all the soccer flows. If weanalyze all the news flows, the SVC layers assigned to pri-ority class 3 for news 1 and news 2 flows are never blockedby the filter because of their good channel quality. Similaranalysis holds for the ice 1 and ice 2 flows. These 4 flows’priority class 3 remains unblocked by the filter throughoutthe simulation period. At the 14.5-s simulation time, thereis a PLR of 0.14. However, system delay and the PLR arenot high enough to block any further flows for priorityclass 3. Therefore, a PLR lower than 3 % appears for theSVC layers 9, 10, 11, and 12 (assigned to priority class 3)for news 1 and news 2 flows.

8.1.1 Performance comparisonwith the proxy basedstrategy

Figure 12 also reports theMOS performance of the proxy-based rate adaptation scheme with M-LWDF schedulerat the eNodeB. According to Fig. 12, most of the videostreaming users achieve better MOS as compared tothe proposed strategy. This is mainly due to the factthat the proxy never overloads the scheduler by consid-ering the channel quality and the throughput require-ments of each flow. Therefore, the input arrival rate atthe eNodeB is always within the achievable rate region.

When the input traffic goes above the input arrival rate,the proxy drops the video layers contributing lowestto the video quality for the flows having poor channelquality.The proxy-based solution requires explicit signaling,

every second, in order to collect the channel quality of allthe video flows from the eNodeB. However, the channelquality of the mobile users can change significantly overthe period of 1 s. Therefore, the proxy can receive out-dated channel quality of the mobile users, which can limitthe video streaming performance. For instance, the aver-age per userMOS (sumMOS, Fig. 12, of all the video flowsdivided by the total number of video flows) for the QoEmarking-based rate adaptation is approximately 3.965compared to 3.992 for the proxy-based rate adaptation.The difference in the average MOS is only 0.027. Thesmaller performance difference mainly stems from thefact that proxy-based rate adaptation has slower conges-tion avoidance frequency in response to the stochasticnature of the wireless channel. On the other hand, thejoint scheduling and filter policy regulates the systemload at a much faster interval (after every tw schedul-ing epochs). Therefore, the proposed strategy has a quickrate adaptation response which suits the highly dynamicmobile environment. The main reason of a slight superiorperformance of the proxy-based scheme is its proactivenature, i.e., it allows the scheduler to run optimally bykeeping the input traffic according to the system capacity.On the other hand, the proposed joint strategy is reac-tive in nature, i.e., the rate adaptation is performed whena delay bound violation occurs. However, because of thequick rate adaptation response (4 cycles per seconds) theperformance penalty in terms of average MOS is minimal,i.e., 0.027.

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Fig. 13 PLR (%) on each of the SVC layers with the proposed joint scheduling and filter policy. Where the PLR value for a layer is not shown in thefigure, the relevant layers have been blocked by the filter


8.1.2 Performance comparisonwith the QoE-aware packetdropping strategy

The QoE-aware packet dropping strategy results in peruser average MOS of 3.8255 as compared to 3.965 for theproposed rule. The dropping algorithm in [18] computesaverage achievable bitrate of each user by considering thedownlink channel quality. Each priority class of the videoflows is assigned a transmission score. The transmissionscore is the product of downlink channel quality and QoEcontribution of the priority class. According to the aver-age achievable bitrate, priority classes of each video floware transferred to the scheduler in the order of their trans-mission score. The remaining priority classes are dropped.The algorithm works at a time scale of 1–2 s. In mobileenvironment, channel quality can change considerablyover the time scale of 1–2 s. Therefore, underutilizationof radio resources occurs when the channel quality ofmobile users is improved after the packet dropping cycle.Similarly, packet delay bound violation occurs when thechannel quality of mobile users fades after the packetdropping cycle. This can result in delay bound violationof high-priority packets since the scheduler is unawareof the priority class importance. On the other hand, theproposed PPS scheduler prioritizes higher priority classesunder congestion. The proposed rule is robust to the fluc-tuations in wireless channel quality by employing a fastrate adaptation cycle. In addition, the hysteresis principleadds stability in the rate adaptation decisions.

8.1.3 Performance comparisonwith the SFG ruleThe average per user MOS for the SFG scheduling ruleis 3.7075 compared to 3.965 for the proposed strategy.

The significant performance difference between the twostrategies is mainly due the fact that the SFG schedul-ing rule is packet delay agnostic. The SFG rule is unableto determine the scheduling urgency of packets nearingthe maximum tolerable delay bound. Another importantpoint to consider is that SFG rule has no prioritizationframework between interactive and streaming video traf-fic. In order to accommodate interactive video, SFG usesthe strict prioritization scheduling rule which reduces thechannel diversity exploitation between the flows of thetwo traffic classes. On the other hand, the PPS schedulingfunction becomes strictly priority aware only under highernormalized system delay. Under congestion, the priorityclass filter reduces the normalized system delay by block-ing least important priority classes which increases thechannel awareness of the scheduling rule, thus increas-ing the channel diversity exploitation between the flows oftwo traffic classes.

8.1.4 Performance comparisonwith the QoE-unaware rulesFor the considered scenario, the performance of the M-LWDF and the Best-CQI scheduling rule with no rateadaptation scheme is shown in Fig. 14. When the systemis left to run under high load, the M-LWDF schedulingrule only serves higher priority flows (video conferencingusers) with good quality. Therefore, all the delay-awarescheduling rules must ensure that the arrival rate shouldnot exceed the system capacity, otherwise the QoS per-formance of the existing users in the network wouldbe violated resulting in an increase in the number ofunsatisfied users. In the considered scenario, the aver-age input arrival rate is 14.8 Mbps as compared to the


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Fig. 14MOS performance for each of the video users under scenario 1 (no rate adaptation strategies are used as the benchmark)


system capacity of 7 Mbps. There is no rate adaptationpolicy for the M-LWDF scheduling rule, therefore thisscheduling rule requires a proper flow admission controlpolicy which should not increase the arrival rate abovethe system capacity. The increase in arrival rate abovethe system capacity incurs delay bound violations. Fur-thermore, the least important packets reside in the buffertill the delay bound, block packets of important priorityclasses. This head-of-line blocking of important prior-ity classes reduces the QoE of all the video streamingflows. Therefore once the delay-sensitive traffic’s arrivalrate reaches the system capacity, the admission controlpolicy should block further flows from entering the sys-tem. Similar analysis holds for the Best-CQI scheduler.However, the Best-CQI scheduler favors the good channelquality flows and results in an unfair system as shown inFig. 14.

8.2 Performance of the proposed and benchmarkstrategies under scenario 2

Figure 15 shows the normalized system delay, sum MOS,and sum throughput performances of the proposed com-posite scheduling rule and the benchmark strategies. Theaverage per user MOS of the proposed rule, withoutbest-effort flows, is 3.965 for 20 delay-sensitive flows asreported in the previous scenario. This decreases to 3.945(sum MOS = 78.9 with C = 50) and 3.917 (sum MOS

= 78.35 with C = 75 and 100) as shown in Fig. 15. Thedecrease in the average MOS with and without best-effortflows is minimal. It is important to note that at lowersystem delays, the priority weight in (20) increases theresource allocation probability of the best-effort flows.Therefore, the normalized system delay of delay-sensitiveflows crosses below the lower threshold for a shorterperiod of time as shown in Fig. 15. This decreases theprobability of re-admission of blocked least importantpriority classes as the re-admission speed depends uponthe normalized system delay of delay-sensitive flows. Thelower the normalized system delay, the higher the prob-ability of re-admission of blocked classes of the delay-sensitive flows.According to the QoEmarking for SVC streaming flows,

the least important priority classes contribute less towardsthe overall QoE. Thus, the impact of the best-effort flowson the QoE of the delay-sensitive flows is negligible.When the prioritization factor increases from 50 to 75,the amount of time normalized system delay remainsbelow the lower threshold is decreased further. The sumMOS of delay-sensitive flows decreases minimally from78.9 to 78.35 with the increase in sum throughput to682.45 kbps. A further increase in the prioritization fac-tor, 75 to 100, has no impact on the performance of thedelay-sensitive and best-effort traffic. This shows that thesystem delay remains between the two thresholds, with

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Fig. 15 Above: normalized system delay performance with and without best-effort flows; below: sum throughput and sum MOS performance forbest-effort and delay-sensitive flows (under scenario 2), respectively


delay-sensitive flows meeting their desired QoS/QoE andbest-effort flows maximizing the system throughput.The figure also reports the performance of the state-

of-the-art composite scheduling rules. M-LWDF rulesachieve higher prioritization for the delay-sensitive flowswith best-effort flows receiving no service. On the otherhand, the log and exponential rules achieve better inter-class fairness. Exponential and the logarithmic schedulingrules reduce the starvation of the best-effort traffic whichcauses an increase in the buffer level of the delay sensitiveflows. The increased queue status of the delay-sensitiveflows is periodically monitored by the proxy, resulting inrate adaptation for the SVC flows at the proxy, whichcauses a decrease in the sum MOS for delay-sensitiveflows.

9 ConclusionsNetwork operators are faced with rapidly growing videotraffic that is becoming a main source of congestion intheir networks. In order to reduce congestion, timelyvideo rate adaptation is required at the RAN. Thisrequires substantial investments on new modules whichcan reduce the congestion through fast rate adaptationof video traffic at the RAN. In this work, we propose anovel PPS, aiming at minimizing delay bound violationsfor themost important priority classes, and a priority classfilter strategy, operating jointly with the scheduler to pro-vide video rate adaptation at the MAC layer. The priorityclass filter utilizes parameters of the scheduling functionand provides fast video rate adaptation at the MAC layer,without requiring additional modules at the RAN. Theproposed framework is capable of reducing congestionand provide high QoE for delay-sensitive as well as thedelay-tolerant traffic classes. Simulation results show thatoperators can provide guaranteed services to high-prioritydelay-sensitive flows marked with the highest priorityclass index. Furthermore, the priority class filtration ofQoE-based SVC classes reduces the resource starvationof best-effort traffic and ensures that best-effort flowsmaximize the system throughput subject to the QoS/QoEconstraints of delay-sensitive flows.

Competing interestsThe authors declare that they have no competing interests.

AcknowledgementsThe research leading to these results has received funding from the EuropeanUnion’s Seventh Framework Programme (FP7) under grant agreement 288502(CONCERTO). The authors would like to acknowledge Prof. Dirk Staehle for thevaluable discussions.

Received: 15 August 2015 Accepted: 10 March 2016

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