research-article

Evaluation of shared resource allocation using SAND for ABR streaming

Authors:

Stefan Pham,

Patrick Heeren,

Daniel Silhavy,

Stefan ArbanowskiAuthors Info & Claims

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

Pages 165 - 174

https://doi.org/10.1145/3304109.3306227

Published: 18 June 2019 Publication History

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Abstract

Adaptive bitrate (ABR) media streaming clients adjust the quality of media content depending on the current network conditions. The Shared Resource Allocation (SRA) feature defined in MPEG-SAND (Server and network assisted DASH), allows servers to allocate bandwidth to streaming clients. This enables coordination and prioritization of clients that are connected to the same network bottleneck, e.g. to maximize the number of clients that can play back a stream fluently. In this paper we evaluate different bandwidth limitation strategies and analyze the effects on the clients. For this purpose, a testbed using multiple Raspberry Pis was created. The results show that in various scenarios, SRA improves the fairness and the QoE of streaming sessions. Solely allocating a maximum quality level to the client is not sufficient in some cases. Therefore, additional means for SRA are evaluated.

References

[1]

2001. tc - Linux manual page. Retrieved October 14, 2018 from http://man7.org/linux/man-pages/man8/tc8.html

Google Scholar

[2]

2015. tc-police - Linux manual page. Retrieved October 14, 2018 from http://man7.org/linux/man-pages/man8/tc-police.8.html

Google Scholar

[3]

Saamer Akhshabi, Lakshmi Anantakrishnan, Ali C. Begen, and Constantine Dovrolis. 2012. What Happens when HTTP Adaptive Streaming Players Compete for Bandwidth? (NOSSDAV '12).

Digital Library

Google Scholar

[4]

Inc. Cisco Systems. 2015. Cisco visual networking index: forecast and methodology, 2016--2021. Retrieved September 14, 2017 from https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html

Google Scholar

[5]

DASH Industry Forum. 2016. DASH-IF Position Paper: Server and Network Assisted DASH (SAND). Retrieved December 14, 2017 from http://dashif.org/wp-content/uploads/2017/01/SAND-Whitepaper-Dec13-final.pdf

Google Scholar

[6]

ISO/IEC 23009-5:2017 2017. Information technology --- Dynamic adaptive streaming over HTTP (DASH) --- Part 5: Server and network assisted DASH (SAND). Standard. International Organization for Standardization, Geneva, CH.

Google Scholar

[7]

Jan Willem Kleinrouweler, Britta Meixner, and Pablo Cesar. 2017. Improving Video Quality in Crowded Networks Using a DANE. In Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, 73--78.

Digital Library

Google Scholar

[8]

Kevin Spiteri, Rahul Urgaonkar, and Ramesh K Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE. IEEE, 1--9.

Crossref

Google Scholar

[9]

Thomas Stockhammer. 2011. Dynamic adaptive streaming over HTTP: standards and design principles. In Proceedings of the second annual ACM conference on Multimedia systems. ACM, 133--144.

Digital Library

Google Scholar

[10]

Emmanuel Thomas, Oskar van Deventer, Thomas Stockhammer, Ali Begen, Mary-Luc Champel, and Ozgur Oyman. 2016. Applications and deployments of Server And Network assisted DASH (SAND).

Google Scholar

[11]

Anatoliy Zabrovskiy, Evgeny Petrov, Evgeny Kuzmin, and Christian Timmerer. 2017. Automated Performance Evaluation of Adaptive HTML5 Player Deployments. In Proceedings of the 21st Conference of Open Innovations Association FRUCT. Helsinki, Finland, Finland.

Google Scholar

Cited By

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Bentaleb ALim MAkcay MBegen AZimmermann R(2024)Bitrate Adaptation and Guidance With Meta Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.337656023:11(10378-10392)Online publication date: Nov-2024
https://doi.org/10.1109/TMC.2024.3376560
Bentaleb ALim MAkcay MBegen AZimmermann R(2023)Meta Reinforcement Learning for Rate AdaptationIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228951(1-10)Online publication date: 17-May-2023
https://doi.org/10.1109/INFOCOM53939.2023.10228951
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

Evaluation of shared resource allocation using SAND for ABR streaming
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming
2. Networks
  1. Network performance evaluation
    1. Network experimentation

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Information & Contributors

Information

Published In

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

June 2019

374 pages

ISBN:9781450362979

DOI:10.1145/3304109

General Chair:
Michael Zink
University of Massachusetts Amherst
,
Program Chairs:
Laura Toni
University College London
,
Ali C. Begen
Özyeğin University/Networked Media

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2019

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MMSys '19

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SIGMM

MMSys '19: 10th ACM Multimedia Systems Conference

June 18 - 21, 2019

Massachusetts, Amherst

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MMSys '19 Paper Acceptance Rate 40 of 82 submissions, 49%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

View all

Bentaleb ALim MAkcay MBegen AZimmermann R(2024)Bitrate Adaptation and Guidance With Meta Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.337656023:11(10378-10392)Online publication date: Nov-2024
https://doi.org/10.1109/TMC.2024.3376560
Bentaleb ALim MAkcay MBegen AZimmermann R(2023)Meta Reinforcement Learning for Rate AdaptationIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228951(1-10)Online publication date: 17-May-2023
https://doi.org/10.1109/INFOCOM53939.2023.10228951
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
Ziegler CNeudel RPham STroudt E(2020)Improving media streaming services for train passengers with 5GProceedings of the 2020 ACM International Conference on Interactive Media Experiences10.1145/3391614.3399399(189-194)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1145/3391614.3399399
Pham SHeeren PSchmidt CSilhavy DArbanowski S(2020)Evaluation of Shared Resource Allocation Using SAND for ABR StreamingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/338892616:2s(1-18)Online publication date: 10-Jul-2020
https://dl.acm.org/doi/10.1145/3388926
Catalan-Cid MCamps-Mur DMontagud MBetzler ACivanlar MSassatelli LLee J(2020)FALCONProceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video10.1145/3386290.3396931(14-20)Online publication date: 10-Jun-2020
https://dl.acm.org/doi/10.1145/3386290.3396931
De Cicco LManfredi GMascolo SPalmisano VAlameda-Pineda, DRedi DCelis PSebe PChang P(2019)QoE-fair Resource Allocation for DASH Video Delivery SystemsProceedings of the 1st International Workshop on Fairness, Accountability, and Transparency in MultiMedia10.1145/3347447.3356753(33-39)Online publication date: 15-Oct-2019
https://dl.acm.org/doi/10.1145/3347447.3356753

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