qoe traffic management for traffic in mobile networks€¦ · allocation to provide best trade-off...

Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group

QoE‐based Traffic Management for Multimedia Traffic in Mobile Networks

Dirk Staehle, DOCOMO Euro‐Labs

staehle@docomolab‐euro.com

Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 2

• Motivation

• DOCOMO‘s QoE framework

• DASH: Dynamic Adaptive Streaming for HTTP

• DOCOMO‘s QoE framework and DASH– concept– results

• Conclusion

Outline


• UPCON: User Plane Congestion Control– 3GPP SA1 study item

• Scope (3GPP TR 22.805)– scenarios and use cases where high usage levels lead to user plane

traffic congestion in the RAN– make efficient use of available resources to increase the potential

number of active users while maintaining the user experience– handling of user plane traffic when RAN congestion occurs based on:

• the subscription of the user• the type of application• the type of content

3GPP Standardization: UPCON

Focus of QoE-based traffic management


• Traffic– total amount of traffic heavily increasing– web, data, video key traffic classes– video dominating

• Devices– increasing diversity – smartphones and notebook dominating

• Smart over‐the‐top applications– application adapt to performance provided by the network– Skype, DASH

Trends in Mobile Data Traffic


• Manage mobile web, data, ADAPTIVE and NON‐ADAPTIVE VIDEO traffic

• Traditional traffic management– differentiate traffic types: web, data, video– 20% interactive web ‐> high priority– 10% data e.g. software updates ‐> low priority– 70% video traffic ‐> no differentiation, one class

• QoE‐based traffic management for multimedia traffic– additionally differentiate based on video and device characteristics – optimize overall QoE for limited radio resources

Challenge


Example VideosHigh

Data Ra

teLow Data Ra

te


Application Sensitivity (QoE) Curves

Reference: Journal of Communications, 2009

Medium Data Rate

Low Data Rate


• Traffic Management Module (Optimization): decides on network resource allocation to provide best trade-off user satisfaction vs network cost– Input: network status, application sensitivity w.r.t. user satisfaction– Output: network resource allocation, feedback to application

RANCore Network

Contentsources

Traffic management(optimizer module)

Traffic engineering(adaptation, traffic shaper/

resource allocation, transcoding, layer dropping)

signaling

Mobileusers

Information gatheringApplication modeling

Network monitoring

QoE‐Based Traffic Management: Concept

• Traffic Engineering Module (enforcement function): adapt data stream to rate determined by optimizer to avoid uncontrolled QoE degradation (stalling, artifacts) due to packet loss or delays at eNB


RANCN

Contentsources

traffic management(optimizer)

Traffic engineering(adaptation, traffic shaper/

resource allocation)

Mobileusers

Network/RAN Monitoring(e.g. NW topology, device/link‘s status)

RNC/eNodeB

PGW

SGW

Applicationsensitivity

1. Data stream + Application sensitivity

3. Application sensitivity+ Network/RAN knowledge

4. Optimal rate adaptation(e.g. 300Kbps instead of

500Kbps)

Signaling

Data stream

5. Forward data stream totraffic engineering module+ signaling of optimal rate

adaptation 6. Return a modified data stream

500 Kbps

50

0 K

bp

s

30

0 K

bp

s

300 Kbps

7. Forward adapted data stream

2. Wireless resources/capacity

Cus

tom

er s

atis

fact

ion

Data rate (kbps)

SGSN

GGSN

xGSN EPC/

QoE‐Based Traffic Management: Architecture

8. Support rates for traffic subject to QoE Management


QoE‐Based Traffic Management and Adaptive HTTP Streaming

• How does end-to-end adaptive streaming solutions work together with network-centric QoE-based traffic optimization framework?

• Does end-to-end adaptation for Over-the-top (OTT) streaming services solve all congestion problems?

Results from cooperation with Prof. Steinbach, Lehrstuhl f. Medientechnik, TUMThanks to Ali El-Essaili and Damien Schröder for result figures


• Pull‐based HTTP streaming technology, client adapts multimedia quality to the rate experienced from the network

• Different proprietary solutionsMicrosoft’s Smooth Streaming, Apple’s HTTP Live Streaming, Adobe HTTP Dynamic Flash Streaming, …

• Standardized by MPEG and 3gpp: MPEG‐DASH, 3GP‐DASH

• Support by Microsoft, Adobe, …• Available clients: Microsoft Silverlight, VLC DASH client,…

Adaptive HTTP Streaming


• Progressive Download, Non‐adaptive HTTP Streaming

Non‐Adaptive HTTP Streaming

http request (video URL)

http response (video file)Video File

Video plays while downloading

Client

Server


Server

Dynamic Adaptive Streaming for HTTP

• Dynamic Adaptive Streaming over HTTP (DASH)

video segments in multiple representations

(formats of different quality and size)

MPD (Multimedia Presentation Description)‐ defines in what format and where the video is stored‐ URLs of video segments of different quality

request for MPDMPD

request for segment 1, representation xsegment k, representation y

request for segment k, representation ysegment k, representation y

Video File

MPDClient

while playing client determines best

representation based on available rate and playout

buffer

First 2s in high quality

Seconds 4-6 in low quality


• Features of QoE Optimization Framework– traffic management (optimizer): determine optimal video quality and

data rates taking into account video characteristics (QoE curve) and long‐term radio channel quality

– traffic engineering (enforcement function): adapt video to rate determined by optimizer to avoid uncontrolled QoE degradation (stalling, artifacts) due to packet loss or delays at eNB

• Features of DASH– adapt video to available bandwidth to avoid uncontrolled QoE

degradation (stalling)

DASH offers lightweight traffic engineering (enforcement) functionality– reactive approach– proactive approach

DASH and QoE‐Based Traffic Management


P-GW

DASH Client

UE

Reactive Approach

eNB

HTTP or Streaming

ServerTE

DASH Proxy

Rate Shaper

HTTP Requests

HTTP Response(Video Segments)

Video RateMPD,

Segment Requests

Optimizer

TMLong‐term channel quality

Video Information(QoE value per representation)

Standard DASH client adapts

requests for rate set by rate shaper

Rate Shaper limits data rate of HTTP download to target

rate set by optimizer


P-GW

DASH Client

UE

Proactive Approach

eNB

HTTP or Streaming

ServerTE

DASH Proxy

Rate Shaper

HTTP Requests

HTTP Response(Video Segments)

Video RateMPD,

Segment Requests

Optimizer

TMLong‐term channel quality

Video Information(QoE value per representation)

Standard DASH client adapts

requests for rate set by rate shaper

Representation

DASH proxy modifies URL of requested segment to

URL of optimal representation

Rate Shaper limits data rate of HTTP download to target rate set by

optimizer


Evaluation Framework

DASH Client

UE

HTTP or Streaming

Server

Proxy

Rate Shaper

HTTP Requests

HTTP Response(MPD, Video Segments)

Microsoft Silverlight,VLC DASH ApacheSquid Proxy Dummynet

Live

Channel Traces QoE CurvesScheduler / OptimizerSimulation

representation per segment

data rate per 2ms interval

Offline


• 8 different videos• 50 replications with different channel traces• Evaluation of

– video specific MOS (averaged over segments) – mean MOS (averaged over segments and videos)

• Comparison of– pure DASH (Non‐Opt)– reactive approach, only rate shaper (QoE reactive)– proactive approach

• rate shaper + proxy, opt. on continuous QoE curves (QoE‐Proxy)• rate shaper + proxy, opt. on discrete QoE points sets (QoE‐Proxy‐d)

– server based approach, optimal encoding at server (QoE‐Server)

Scenario


Mean MOS

Potential Gain of 0.5 in Mean MOS by QoE framework

Gain of 0.25 in Mean MOS by proactive approach


Individual MOS

Almost equal Mean MOS for non-demanding videos

Clear Mean MOS improvement for demanding videos


Subjective Tests


Overall results

30 km/h 120 km/h

Mean MOS improves

MOS range gets smaller

(“fairer”)

Proactive Reactive DASH Proactive Reactive DASH


• Trends in mobile multimedia communication– video is/becomes dominating traffic type– smart applications like adaptive HTTP streaming

• QoE based traffic management framework– differentiates multimedia content based on its inherent characteristics– efficiently utilizes radio resources for an overall optimal QoE– flexible to integrate new streaming technologies

• End‐to‐end adaptive streaming (DASH)– is a big step for high‐quality mobile multimedia delivery but – rate allocation depends on eNB scheduler such that – QoE‐based traffic management achieves significant gain, in particular

for demanding videos

Conclusion


DOCOMO Communications Laboratories Europe GmbHLandsberger Strasse 312 – 80687 Munich, GermanyPhone: +49 (89) 56824‐0 | www.docomolab‐euro.com

Dr. Dirk Staehlestaehle@docomolab‐euro.com

qoe traffic management for traffic in mobile networks€¦ · allocation to provide best trade-off...

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