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Downton abbey without the hiccups: buffer-based rate adaptation for HTTP video streaming

Authors:

Te-Yuan Huang,

Ramesh Johari,

Nick McKeownAuthors Info & Claims

FhMN '13: Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking

Pages 9 - 14

https://doi.org/10.1145/2491172.2491179

Published: 16 August 2013 Publication History

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Abstract

Recent work has shown how hard it is to pick a video streaming rate. Video service providers use heuristics to estimate the network capacity leading to unnecessary rebuffering events and suboptimal video quality. This paper argues that we should do away with estimating network capacity, and instead directly observe and control the playback buffer. We present a class of rate selection algorithms that allow us to optimize the delivered video quality while provably never unnecessarily rebuffering. Our algorithms work with discrete video rates, video chunking and for both CBR and VBR video codecs.

References

[1]

S. Akhshabi et al. An Experimental Evaluation of Rate Adaptation Algorithms in Adaptive Streaming over HTTP. In ACM MMSys, 2011.

Digital Library

Google Scholar

[2]

S. Akhshabi et al. What Happens When HTTP Adaptive Streaming Players Compete for Bandwidth? In ACM NOSSDAV, June 2012.

Digital Library

Google Scholar

[3]

A. Balachandran et al. A Quest for an Internet Video Quality-of-Experience Metric. In ACM HotNets-XI, 2012.

Digital Library

Google Scholar

[4]

L. De Cicco et al. Feedback Control for Adaptive Live Video Streaming. In ACM MMSys, 2011.

Digital Library

Google Scholar

[5]

T.-Y. Huang et al. Confused, Timid, and Unstable: Picking a Video Streaming Rate is Hard. In ACM IMC, November 2012.

Digital Library

Google Scholar

[6]

T.-Y. Huang et al. Buffer-based rate adaptation for http video streaming. Technical report, 2013. http://goo.gl/mD1uM.

Google Scholar

[7]

Sandvine: Global Internet Phenomena Report. http://www.sandvine.com/news/pr\_detail.asp?ID=312.

Google Scholar

[8]

G. Tian and Y. Liu. Towards Agile and Smooth Video Adaptation in Dynamic HTTP Streaming. In ACM CoNEXT, December 2012.

Digital Library

Google Scholar

Cited By

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Alhilal ABraud THan BHui P(2022)Nebula: Reliable Low-latency Video Transmission for Mobile Cloud GamingProceedings of the ACM Web Conference 202210.1145/3485447.3512276(3407-3417)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512276
Xu YYin JYang QYang L(2022)Media Production Using Cloud and Edge Computing: Recent Progress and NBMP-Based ImplementationIEEE Transactions on Broadcasting10.1109/TBC.2022.314070468:2(545-558)Online publication date: Jun-2022
https://doi.org/10.1109/TBC.2022.3140704
Sani YQuinlan JSreenan C(2022)A bio-inspired managed video delivery service using HTTP-based adaptive streamingMultimedia Systems10.1007/s00530-022-00894-x28:3(1083-1097)Online publication date: 14-Feb-2022
https://doi.org/10.1007/s00530-022-00894-x
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Index Terms

Downton abbey without the hiccups: buffer-based rate adaptation for HTTP video streaming
1. Hardware
  1. Communication hardware, interfaces and storage
2. Networks

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Published In

FhMN '13: Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking

August 2013

68 pages

ISBN:9781450321839

DOI:10.1145/2491172

General Chairs:
Eduardo Cerqueira
Federal University of Para
,
Andreas Mauthe
Lancaster University
,
Marilia Curado
University of Coimbra
,
Mikoŏaj Leszczuk
AGH University of Science and Technology
,
Program Chairs:
Fernando Boavida
University of Coimbra
,
Eng Keong Lua
Monash University

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2013

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SIGCOMM'13

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SIGCOMM

SIGCOMM'13: ACM SIGCOMM 2013 Conference

August 16, 2013

Hong Kong, China

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FhMN '13 Paper Acceptance Rate 9 of 20 submissions, 45%;

Overall Acceptance Rate 9 of 20 submissions, 45%

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Cited By

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Alhilal ABraud THan BHui P(2022)Nebula: Reliable Low-latency Video Transmission for Mobile Cloud GamingProceedings of the ACM Web Conference 202210.1145/3485447.3512276(3407-3417)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512276
Xu YYin JYang QYang L(2022)Media Production Using Cloud and Edge Computing: Recent Progress and NBMP-Based ImplementationIEEE Transactions on Broadcasting10.1109/TBC.2022.314070468:2(545-558)Online publication date: Jun-2022
https://doi.org/10.1109/TBC.2022.3140704
Sani YQuinlan JSreenan C(2022)A bio-inspired managed video delivery service using HTTP-based adaptive streamingMultimedia Systems10.1007/s00530-022-00894-x28:3(1083-1097)Online publication date: 14-Feb-2022
https://doi.org/10.1007/s00530-022-00894-x
Rahman WHossain MHuh E(2021)Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH VideoApplied Sciences10.3390/app1111527011:11(5270)Online publication date: 6-Jun-2021
https://doi.org/10.3390/app11115270
Gatimu KLee B(2021)qMDP: DASH Adaptation using Queueing Theory within a Markov Decision Process2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC49032.2021.9369481(1-6)Online publication date: 9-Jan-2021
https://doi.org/10.1109/CCNC49032.2021.9369481
Zhang TRen FCheng WLuo XShu RLiu X(2020)Towards Influence of Chunk Size Variation on Video Streaming in Wireless NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2019.291275019:7(1715-1730)Online publication date: 1-Jul-2020
https://doi.org/10.1109/TMC.2019.2912750
Yuan HHu XHou JWei XKwong S(2020)An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTPIEEE Transactions on Broadcasting10.1109/TBC.2019.295407466:2(251-263)Online publication date: Jun-2020
https://doi.org/10.1109/TBC.2019.2954074
Yuan HZhao SHou JWei XKwong S(2020)Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree VideosIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2019.295798114:1(177-193)Online publication date: Jan-2020
https://doi.org/10.1109/JSTSP.2019.2957981
Shen MZhang JXu KZhu LLiu JDu X(2020)DeepQoE: Real-time Measurement of Video QoE from Encrypted Traffic with Deep Learning2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)10.1109/IWQoS49365.2020.9212897(1-10)Online publication date: Jun-2020
https://doi.org/10.1109/IWQoS49365.2020.9212897
Zhang TRen FWang B(2020)Modeling and Analyzing Live Streaming Performance2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)10.1109/IWQoS49365.2020.9212876(1-10)Online publication date: Jun-2020
https://doi.org/10.1109/IWQoS49365.2020.9212876
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A buffer-based approach to rate adaptation: evidence from a large video streaming service

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