a comparative case study of http adaptive streaming ... · outline introduction motivation ......

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∗Mathematical and Algorithmic Sciences LabFrance Research Center

Huawei Technologies France SASU

A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks

Theodoros Karagkioules∗†, Cyril Concolato †, Dimitrios Tsilimantos ∗ and

Stefan Valentin∗

†LTCI Telecom ParisTech

Universite Paris-Saclay

T. Karagkioules et al. A Comparative Case Study of HTTP Adaptive Streaming

Algorithms in Mobile Networks


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Outline Introduction

Motivation

Scope

Adaptive Bitrate Algorithms

Throughput-based adaptation

Buffer-based adaptation

Time-based adaptation

Experimental Framework

Network profiles

QoE

Content

Performance evaluation

Experimental Validation

Adaptability

Instability

Un-smoothness

Overview

Conclusion




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Motivation Video made 60% of all mobile data traffic in 2016, predicted to increase to 78% by

2021 [1]

This traffic is dominated by the Dynamic Adaptive Streaming over HTTP a.k.a MPEG-DASH [2]

A HAS video stream is available in multiple qualities, each divided into segments or chunks

Segments are downloaded using HTTP on top of TCP or UDP (in combination with QUIC)

Video client controls bitrate by adapting (i) video quality and (ii) segment download time

Low

Medium

High

Quality

Time

Request for

specific quality

Network

conditions

Bandwidth

Time

Time

Quality

Low

Medium

High

Web server with various

qualities per segment

Received segments at

streaming client

HAS policy not

standardized

[1] Cisco, “Visual Networking Index: Forecast and Methodology, 2016-2021”, White Paper, Feb. 2017

[2] ISO/IEC, “Dynamic adaptive streaming over HTTP (DASH),” International Standard DIS 23009-1.2, ISO/IEC, 2012




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Scope

Extensive state of the art in literature

Not only heuristics

Research is shifting towards control theoretic approaches

Classification of ABR algorithms

Throughput-based

Buffer-based

Time-based

Too many comparisons in literature?

A Comparison per class is missing

Also a comparison that takes the application type (VoD and Live streaming) is missing

Performance evaluation of 5 algorithms, based or real field mobile network data




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Motivation

Scope






Network profiles

QoE

Content



Adaptability

Instability

Un-smoothness

Overview

Conclusion




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Throughput-based adaptation In throughput-based adaptation the decision on the bit-rate and the scheduling of

every segment happens in four steps:

1. Throughput estimation

1. Where the TCP throughput is estimated through probes

2. Smoothing

1. Where the estimated TCP throughput is smoothed, to avoid estimation errors

3. Quantization

1. Where the smoothed estimated throughput is mapped to the discrete set of the video representations

4. Scheduling

1. The next segment request us scheduled once the inter-request time is estimated.

In our study we have implemented two algorithms (Conventional and PANDA) as specified in [3]

[3] Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran. 2014. Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale. IEEE J. Sel. Areas Commun. 32 (April 2014).




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Buffer based adaptation

In buffer-based adaptation the decision on the bit-rate and the scheduling of every segment is made based on the instantaneous buffer level.

According to BBA [4] the average segment size of each corresponding bitrate is mapped with the instantaneous buffer level, in a linear manner with two fixed points for the lowest and highest bit-rate.

BOLA [5] deploys Lyapunov optimization in order to indicate the video bit-rate of each segment. The algorithm is designed to maximize a joint utility function that rewards an increase in the average quality and penalizes potential re-buffering occurrences.

[4] Te-Yuan Huang, Ramesh Johari, Nick McKeown, Mathew Trunnell, and Mark Watson. 2014. A Buffer-based Approach to Rate Adaptation: Evidence from aLarge Video Streaming Service. In Proc. ACM SIGCOMM. [5] K. Spiteri, R. Urgaonkar, and R. K. Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In IEEE INFOCOM




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Since the download time is considered as a higher level parameter than throughput, in this study, time-based adaptation is treated as a separate class of algorithms.

ABMA+ [6] is an adaptation and buffer management algorithm, which selects the video representation based on the predicted probability of video stalling.

The algorithm continuously estimates the segment download time and uses a pre-computed play-out buffer map to select the maximum video representation, which guarantees smooth content play-out.

The segment download time estimation is based on the same probing mechanism as the throughput-based method

[6] A. Beben, P. Wisniewski, J. Mongay Batalla, and P. Krawiec. 2016. ABMA+: Lightweight and Effecient Algorithm for HTTP Adaptive Streaming. In Proc. Int.ACM Conference on Multimedia Systems (MMSys). 2:1–2:11.




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Motivation

Scope






Network profiles

QoE

Content



Adaptability

Instability

Un-smoothness

Overview

Conclusion




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Network profiles

Mobile networks are characterized by their intense bandwidth and coverage fluctuation

To test the realistic performance of the ABR algorithms we used actual 3G traces [7].

2 different network profiles where studied to emulate all coverage situations:

Challenging (Underground)

Normal (Bus)

1 Controlled (DASH-IF) scenario was used for the experimental validation

[7] C. Griwodz P. Halvorsen H. Riiser, P. Vigmostad. 2013. Commute Path Bandwidth Traces from 3G Networks: Analysis and Applications. Proc. of MMSys 5, 1 (March 2013), 114–118.




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QoE metrics (1/2)

A unified QoE model is missing from the literature.

In this study we considered 5 metrics that can be combined in 3 categories

For a video of K segments and a set of video bitrates R ϵ {𝑅1,…, 𝑅𝑁}, the QoE is characterized as:

Adaptability:

Instability, which consists of the adaptation frequency (AF) and the adaptation amplitude (AA):




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QoE metrics (2/2)

Un-smoothness, which consists of the re-buffering duration and the rebuffering frequency.

Where L is the length of the video, ω is the rebuffering threshold and β a binary operator regarding the occurrence of a rebuffering event.

All the metrics are normalized and averaged over the complete set of traces.




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Motivation

Scope






Network profiles

QoE

Content



Adaptability

Instability

Unsmoothness

Overview

Conclusion




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5 ABR algorithms (Conventional, PANDA, BBA, BOLA, ABMA+)

12 mobile traces and 2 network profiles

5 Challenging scenarios

7 Normal scenarios

3 different movies (encoded according to the quintiles of the total throughput CDF)

Big buck bunny (Animated content)

Tears of Steel (High motion animated and non animated scenes)

Red bull play streets (Sport)

Both live and VoD is investigated in our study as 𝐵𝑚𝑎𝑥 is considered as an additional factor

VoD (𝐵𝑚𝑎𝑥=4 segments)

Live (𝐵𝑚𝑎𝑥=23 segments)

5 QoE metrics (Adaptability, Instability (2), Unsmoothness (2))




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Adaptability

• Throughput-based and time-based algorithms show a slightly diminished ability to match the representation to the available average throughput

• This is due to the significant throughput variation that characterizes the selected network profiles.

• Buffer-based algorithms achieve higher adaptability in normal conditions.

• They are more successful, by design, in conserving high representation levels, even higher than the available throughput.




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Instability • Buffer-based algorithms are about 40% more

probable of making a quality switch when the buffer is small.

• BOLA is optimized to achieve a high bit-rate but, the stability aspect is not considered in the optimization, since it is addressed with a heuristic in a second phase.

• BBA has a pre-selected constant higher buffer threshold which makes the segment map less agile to throughput variation when the maximum buffer is small.

• Throughput-based and time-based algorithms appear to switch quality less often.




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Unsmoothness • Re-buffering probability is slightly higher in

the cases of a small buffer (i.e. live streaming).

• Challenging profiles: buffer-based algorithms, along with PANDA, are slightly more probable to experience a re-buffering event.

• Normal scenarios: smooth streaming from almost all algorithms, due to the absence of long throughput outages of this profile.

• The duration (amplitude) of the re-buffering events, lasts about 25% of the video duration. (underground areas, may cause network outages, for 1/4 of the trace.)




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General comments on the results • No metric should be treated separately

• Only the combination of all metrics allows our comparison to be insightful.

• Overall, our results match those in literature

• In Table 2 we have gathered the best performing classes of algorithms, per QoE

element.

• This table can serve as insight to the selection of the most appropriate algorithmic class, depending on the application parameters (live, VOD, etc.) and the commonly experienced network conditions.




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Motivation

Scope






Network profiles

QoE

Content



Adaptability

Instability

Un-smoothness

Overview

Conclusion




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Conclusion Per class comparison of 5 state-of-the-art ABR algorithms

Studied Normal and challenging network situations

LIVE and VoD

Unified QoE framework missing

Optimized parameterization is necessary

The target buffer level is a critical classifier for the studied HAS algorithms.

Overall, we believe that our findings provide valuable insight for the design and choice of HAS algorithms according to networks conditions and service requirements.

Future work!

Future work!




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Thank you

For any questions and/or comments/remarks please contact me at:

[email protected]

a comparative case study of http adaptive streaming ... · outline introduction motivation ......

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