research-article

Online learning for low-latency adaptive streaming

Authors:

Theo Karagkioules,

Rufael Mekuria,

Dirk Griffioen,

Arjen WagenaarAuthors Info & Claims

MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference

Pages 315 - 320

https://doi.org/10.1145/3339825.3397042

Published: 27 May 2020 Publication History

Editorial Notes

A corrigendum was issued for this paper on June 16, 2020. You can download the corrigendum from the supplemental material section of this citation page.

Abstract

Achieving low-latency is paramount for live streaming scenarios, that are now-days becoming increasingly popular. In this paper, we propose a novel algorithm for bitrate adaptation in HTTP Adaptive Streaming (HAS), based on Online Convex Optimization (OCO). The proposed algorithm, named Learn2Adapt-LowLatency (L2A-LL), is shown to provide a robust adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, throughput estimation or application-specific adjustments. These properties make it very suitable for users who typically experience fast variations in channel characteristics. The proposed algorithm has been implemented in DASH-IF's reference video player (dash.js) and has been made publicly available for research purposes at [22]. Real experiments show that L2A-LL reduces latency significantly, while providing a high average streaming bit-rate, without impairing the overall Quality of Experience (QoE); a result that is independent of the channel and application scenarios. The presented optimization framework, is robust due to its design principle; its ability to learn and allows for modular QoE prioritization, while it facilitates easy adjustments to consider applications beyond live streaming and/or multiple user classes.

Supplementary Material

p315-karagkioules-corrigendum (p315-karagkioules-corrigendum.pdf)

Corrigendum to "Online learning for low-latency adaptive streaming" by Karagkioules et al., Proceedings of the 11th ACM Multimedia Systems Conference (MMSys '20).

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References

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Cited By

Seufert MSpangenberger MPoignée FWamser FRobitza WTimmerer CHoßfeld T(2024)COBIRAS: Offering a Continuous Bit Rate Slide to Maximize DASH Streaming Bandwidth UtilizationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367737920:10(1-24)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3677379
Bi PZou YXiao MYu DLi YLiu ZXie QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)LiteQUIC: Improving QoE of Video Streams by Reducing CPU Overhead of QUICProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681670(7918-7927)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681670
Fang HZhao HShi JZhang MWu GChou YWang FLiu JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Robust Live Streaming over LEO Satellite Constellations: Measurement, Analysis, and Handover-Aware AdaptationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680712(5958-5966)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680712
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Index Terms

Online learning for low-latency adaptive streaming
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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Published In

cover image ACM Conferences

MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference

May 2020

403 pages

ISBN:9781450368452

DOI:10.1145/3339825

General Chairs:
Laura Toni
University College London
,
Ali C. Begen
Ozyegin University and Networked Media
,
Program Chairs:
Özgü Alay
University of Oslo and Simula Metropolitan
,
Christian Timmerer
Alpen-Adria-Universität Klagenfurt and Bitmovin

Copyright © 2020 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 the author(s) 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: 27 May 2020

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MMSys '20: 11th ACM Multimedia Systems Conference

June 8 - 11, 2020

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MMSys '20 Paper Acceptance Rate 18 of 55 submissions, 33%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Seufert MSpangenberger MPoignée FWamser FRobitza WTimmerer CHoßfeld T(2024)COBIRAS: Offering a Continuous Bit Rate Slide to Maximize DASH Streaming Bandwidth UtilizationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367737920:10(1-24)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3677379
Bi PZou YXiao MYu DLi YLiu ZXie QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)LiteQUIC: Improving QoE of Video Streams by Reducing CPU Overhead of QUICProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681670(7918-7927)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681670
Fang HZhao HShi JZhang MWu GChou YWang FLiu JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Robust Live Streaming over LEO Satellite Constellations: Measurement, Analysis, and Handover-Aware AdaptationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680712(5958-5966)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680712
K. Fezeu RFiandrino CRamadan ECarpenter Jde Freitas LBilal FYe WWidmer JQian FZhang ZSekar VYu MSeneviratne AVeitch D(2024)Unveiling the 5G Mid-Band Landscape: From Network Deployment to Performance and Application QoEProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672269(358-372)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672269
Lv GWu QLiu YLi ZTan QYang FChen WMa YGuo HChen YXie GGanesan DShi W(2024)Chorus: Coordinating Mobile Multipath Scheduling and Adaptive Video StreamingProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649359(246-262)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3636534.3649359
Radler SPrüller LArtioli ETashtarian FTimmerer C(2024)PyStreamProceedings of the 15th ACM Multimedia Systems Conference10.1145/3625468.3652194(464-470)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3625468.3652194
Zhao JPan J(2024)Low-Latency Live Video Streaming over a Low-Earth-Orbit Satellite Network with DASHProceedings of the 15th ACM Multimedia Systems Conference10.1145/3625468.3647616(109-120)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3625468.3647616
Liang YLyko TNilsson MFarrow PAppleby SBroadbent MRace N(2024)Playing Catch-Up: Evaluating Playback Speed Control in Low-Latency Live Streaming2024 16th International Conference on Quality of Multimedia Experience (QoMEX)10.1109/QoMEX61742.2024.10598290(270-273)Online publication date: 18-Jun-2024
https://doi.org/10.1109/QoMEX61742.2024.10598290
Lyko TElkhatib YRamdhany RRace N(2024)Drop or Stop: Investigating the Impact of Playback Rate on QoE in Adaptive Video Streaming2024 16th International Conference on Quality of Multimedia Experience (QoMEX)10.1109/QoMEX61742.2024.10598252(111-117)Online publication date: 18-Jun-2024
https://doi.org/10.1109/QoMEX61742.2024.10598252
Khan ITran THiltunen MKaragioules TKoutsonikolas D(2024)An Experimental Study of Low-Latency Video Streaming over 5G2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)10.1109/MeditCom61057.2024.10621182(383-388)Online publication date: 8-Jul-2024
https://doi.org/10.1109/MeditCom61057.2024.10621182
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