research-article

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

Authors:

Theodoros Karagkioules,

Cyril Concolato,

Dimitrios Tsilimantos,

Stefan ValentinAuthors Info & Claims

NOSSDAV'17: Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video

Pages 1 - 6

https://doi.org/10.1145/3083165.3083170

Published: 20 June 2017 Publication History

Abstract

HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for video delivery in mobile networks. Over the past few years, several HAS algorithms have been introduced in order to improve user quality-of-experience (QoE) by bit-rate adaptation. Their difference is mainly the required input information, ranging from network characteristics to application-layer parameters such as the playback buffer. Interestingly, despite the recent outburst in scientific papers on the topic, a comprehensive comparative study of the main algorithm classes is still missing. In this paper we provide such comparison by evaluating the performance of the state-of-the-art HAS algorithms per class, based on data from field measurements. We provide a systematic study of the main QoE factors and the impact of the target buffer level We conclude that this target buffer level is a critical classifier for the studied HAS algorithms. While buffer-based algorithms show superior QoE in most of the cases, their performance may differ at the low target buffer levels of live streaming services. Overall, we believe that our findings provide valuable insight for the design and choice of HAS algorithms according to networks conditions and service requirements.

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Cited By

Karagkioules TPaschos GLiakopoulos NFiandrotti ATsilimantos DCagnazzo M(2022)Online Learning for Adaptive Video Streaming in Mobile NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/346081918:1(1-22)Online publication date: 27-Jan-2022
https://dl.acm.org/doi/10.1145/3460819
Esper JClaudia Bastos Loureiro Moncao AChaves Rodrigues KBonato Both CCorrea SVieira Cardoso K(2022)QoE-DASH: DASH QoE Performance Evaluation Tool for Edge-Cache and RecommendationICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9839234(757-762)Online publication date: 16-May-2022
https://doi.org/10.1109/ICC45855.2022.9839234
Lopes RRocha FSargento SLuís MLeitão RMarques EAntunes B(2022)A multi-layer probing approach for video over 5G in vehicular scenariosVehicular Communications10.1016/j.vehcom.2022.10053438:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.vehcom.2022.100534
Show More Cited By

Index Terms

A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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cover image ACM Conferences

NOSSDAV'17: Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video

June 2017

105 pages

ISBN:9781450350037

DOI:10.1145/3083165

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 June 2017

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June 20 - 23, 2017

Taipei, Taiwan

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NOSSDAV'17 Paper Acceptance Rate 15 of 40 submissions, 38%;

Overall Acceptance Rate 118 of 363 submissions, 33%

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Cited By

Karagkioules TPaschos GLiakopoulos NFiandrotti ATsilimantos DCagnazzo M(2022)Online Learning for Adaptive Video Streaming in Mobile NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/346081918:1(1-22)Online publication date: 27-Jan-2022
https://dl.acm.org/doi/10.1145/3460819
Esper JClaudia Bastos Loureiro Moncao AChaves Rodrigues KBonato Both CCorrea SVieira Cardoso K(2022)QoE-DASH: DASH QoE Performance Evaluation Tool for Edge-Cache and RecommendationICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9839234(757-762)Online publication date: 16-May-2022
https://doi.org/10.1109/ICC45855.2022.9839234
Lopes RRocha FSargento SLuís MLeitão RMarques EAntunes B(2022)A multi-layer probing approach for video over 5G in vehicular scenariosVehicular Communications10.1016/j.vehcom.2022.10053438:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.vehcom.2022.100534
Stepanovic MBjelica MKovacevic BZivkov D(2021)Smart Delay Module For In-Vehicle Live IPTV Streaming2021 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE50685.2021.9427741(1-3)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICCE50685.2021.9427741
Ul Mustafa RIslam MRothenberg CFerlin SRaca DQuinlan J(2020)DASH QoE Performance Evaluation Framework with 5G Datasets2020 16th International Conference on Network and Service Management (CNSM)10.23919/CNSM50824.2020.9269111(1-6)Online publication date: 2-Nov-2020
https://doi.org/10.23919/CNSM50824.2020.9269111
Guo JGong XLiang JWang WQue X(2020)EAAT: Environment-Aware Adaptive Transmission for Split-Screen Video StreamingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.295513630:11(4355-4367)Online publication date: Nov-2020
https://doi.org/10.1109/TCSVT.2019.2955136
Vlaovic JZagar DRimac-Drlje SFilipovic L(2020)Comparison of representation switching number and achieved bit-rate in DASH algorithms2020 International Conference on Smart Systems and Technologies (SST)10.1109/SST49455.2020.9264069(17-22)Online publication date: 14-Oct-2020
https://doi.org/10.1109/SST49455.2020.9264069
Storck CDuarte-Figueiredo F(2020)A Performance Analysis of Adaptive Streaming Algorithms in 5G Vehicular Communications in Urban Scenarios2020 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC50000.2020.9219682(1-7)Online publication date: Jul-2020
https://doi.org/10.1109/ISCC50000.2020.9219682
Duan JLi KHe YZhang MYang X(2020)A Bandwidth-Aware Video Segments Request Strategy to Optimize User’s QoE in Connected Vehicle NetworksIEEE Access10.1109/ACCESS.2020.30042288(117493-117502)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3004228
Schwind AMidoglu CAlay ÖGriwodz CWamser F(2020)Dissecting the performance of YouTube video streaming in mobile networksInternational Journal of Network Management10.1002/nem.205830:3Online publication date: 14-May-2020
https://dl.acm.org/doi/10.1002/nem.2058
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