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Cross-Layer Effects on Training Neural Algorithms for Video Streaming

Authors:

Pablo Gil Pereira,

Andreas Schmidt,

Thorsten HerfetAuthors Info & Claims

NOSSDAV '18: Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video

Pages 43 - 48

https://doi.org/10.1145/3210445.3210453

Published: 12 June 2018 Publication History

Abstract

Nowadays Dynamic Adaptive Streaming over HTTP (DASH) is the most prevalent solution on the Internet for multimedia streaming and responsible for the majority of global traffic. DASH uses adaptive bit rate (ABR) algorithms, which select the video quality considering performance metrics such as throughput and playout buffer level. Pensieve is a system that allows to train ABR algorithms using reinforcement learning within a simulated network environment and is outperforming existing approaches in terms of achieved performance. In this paper, we demonstrate that the performance of the trained ABR algorithms depends on the implementation of the simulated environment used to train the neural network. We also show that the used congestion control algorithm impacts the algorithms' performance due to cross-layer effects.

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Cited By

Jia LZhou CHuang TLi CSun L(2024)Dancing with Shackles, Meet the Challenge of Industrial Adaptive Streaming via Offline Reinforcement LearningIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621126(2169-2178)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621126
Huang TZhang RWu CSun LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Optimizing Adaptive Video Streaming with Human FeedbackProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611771(1707-1718)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611771
Gil Pereira PVogelgesang KMiodek MSchmidt AHerfet T(2023)DeepSHARQ: hybrid error coding using deep learningJournal of Reliable Intelligent Environments10.1007/s40860-023-00207-79:3(283-301)Online publication date: 14-Jun-2023
https://doi.org/10.1007/s40860-023-00207-7
Show More Cited By

Index Terms

Cross-Layer Effects on Training Neural Algorithms for Video Streaming
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming
2. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network control algorithms
  2. Network performance evaluation
    1. Network experimentation
    2. Network measurement

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Information & Contributors

Information

Published In

cover image ACM Conferences

NOSSDAV '18: Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video

June 2018

84 pages

ISBN:9781450357722

DOI:10.1145/3210445

General Chair:
Ketan Mayer-Patel
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Chuan Wu
University of Hong Kong, Hong Kong
,
Nabil J. Sarhan
Wayne State University, Detroit, USA

Copyright © 2018 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2018

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Research-article
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Refereed limited

Conference

MMSys '18

Sponsor:

SIGMM

MMSys '18: 9th ACM Multimedia Systems Conference

June 12 - 15, 2018

Amsterdam, Netherlands

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Overall Acceptance Rate 118 of 363 submissions, 33%

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Cited By

Jia LZhou CHuang TLi CSun L(2024)Dancing with Shackles, Meet the Challenge of Industrial Adaptive Streaming via Offline Reinforcement LearningIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621126(2169-2178)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621126
Huang TZhang RWu CSun LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Optimizing Adaptive Video Streaming with Human FeedbackProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611771(1707-1718)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611771
Gil Pereira PVogelgesang KMiodek MSchmidt AHerfet T(2023)DeepSHARQ: hybrid error coding using deep learningJournal of Reliable Intelligent Environments10.1007/s40860-023-00207-79:3(283-301)Online publication date: 14-Jun-2023
https://doi.org/10.1007/s40860-023-00207-7
Yuan DZhang YZhang WLiu XDu HZheng QYan ZZink MLiu Y(2022)PRIORProceedings of the 32nd Workshop on Network and Operating Systems Support for Digital Audio and Video10.1145/3534088.3534348(36-42)Online publication date: 17-Jun-2022
https://dl.acm.org/doi/10.1145/3534088.3534348
Yun WKwon DChoi MKim JCaire GMolisch A(2022)Quality-Aware Deep Reinforcement Learning for Streaming in Infrastructure-Assisted Connected VehiclesIEEE Transactions on Vehicular Technology10.1109/TVT.2021.313445771:2(2002-2017)Online publication date: Feb-2022
https://doi.org/10.1109/TVT.2021.3134457
Huang TZhou CZhang RWu CSun L(2022)Learning Tailored Adaptive Bitrate Algorithms to Heterogeneous Network Conditions: A Domain-Specific Priors and Meta-Reinforcement Learning ApproachIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.318080440:8(2485-2503)Online publication date: Aug-2022
https://doi.org/10.1109/JSAC.2022.3180804
Sani YRaca DQuinlan JSreenan C(2020)SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)10.1109/QoMEX48832.2020.9123139(1-6)Online publication date: May-2020
https://doi.org/10.1109/QoMEX48832.2020.9123139
Huang TZhou CYao XZhang RWu CYu BSun L(2020)Quality-Aware Neural Adaptive Video Streaming With Lifelong Imitation LearningIEEE Journal on Selected Areas in Communications10.1109/JSAC.2020.300036338:10(2324-2342)Online publication date: Oct-2020
https://doi.org/10.1109/JSAC.2020.3000363
Huang TSun L(2020)Deepmpc: A Mixture Abr Approach Via Deep Learning And Mpc2020 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP40778.2020.9191198(1231-1235)Online publication date: Oct-2020
https://doi.org/10.1109/ICIP40778.2020.9191198
Huang TZhou CZhang RWu CYao XSun LAmsaleg LHuet BLarson MGravier GHung HNgo CTsang Ooi W(2019)Comyco: Quality-Aware Adaptive Video Streaming via Imitation LearningProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3351014(429-437)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3343031.3351014

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