research-article

Design and Analysis of QoE-Aware Quality Adaptation for DASH: A Spectrum-Based Approach

Authors:

Divyashri Bhat,

Michael ZinkAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 13, Issue 3s

Article No.: 45, Pages 1 - 24

https://doi.org/10.1145/3092839

Published: 14 July 2017 Publication History

Abstract

The dynamics of the application-layer-based control loop of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection for DASH a difficult problem. In this work, we provide a DASH quality adaptation algorithm, named SQUAD, that is specifically tailored to provide a high quality of experience (QoE). We review and provide new insights into the challenges for DASH rate estimation. We found that in addition to the ON-OFF behavior of DASH clients, there exists a discrepancy in the timescales that form the basis of the rate estimates across (i) different video segments and (ii) the rate control loops of DASH and Transmission Control Protocol (TCP). With these observations in mind, we design SQUAD aiming to maximize the average quality bitrate while minimizing the quality variations. We test our implementation of SQUAD together with a number of different quality adaptation algorithms under various conditions in the Global Environment for Networking Innovation testbed, as well as, in a series of measurements over the public Internet. Through a measurement study, we show that by sacrificing little to nothing in average quality bitrate, SQUAD can provide significantlygt; better QoE in terms of quality switching and magnitude. In addition, we show that retransmission of higher-quality segments that were originally received in low-quality is feasible and improves the QoE.

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Cited By

Rudolph MSchneegass SRizk A(2024)Transcoding V-PCC Point Cloud Streams in Real-timeACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3682062Online publication date: Aug-2024
https://doi.org/10.1145/3682062
Nguyen MAmirpour HTashtarian FTimmerer CHellwagner H(2023)Performance analysis of H2BR: HTTP/2-based segment upgrading to improve the QoE in HASMultimedia Tools and Applications10.1007/s11042-023-15516-583:5(12561-12595)Online publication date: 11-Jul-2023
https://doi.org/10.1007/s11042-023-15516-5
Kiani Mehr SJogalekar PMedhi D(2021)Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clientsJournal of Internet Services and Applications10.1186/s13174-021-00133-y12:1Online publication date: 26-Apr-2021
https://doi.org/10.1186/s13174-021-00133-y
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Index Terms

Design and Analysis of QoE-Aware Quality Adaptation for DASH: A Spectrum-Based Approach
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 13, Issue 3s

Special Section on Deep Learning for Mobile Multimedia and Special Section on Best Papers from ACM MMSys/NOSSDAV 2016

August 2017

258 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3119899

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2017 ACM.

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Association for Computing Machinery

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Publication History

Published: 14 July 2017

Accepted: 01 March 2017

Revised: 01 January 2017

Received: 01 November 2016

Published in TOMM Volume 13, Issue 3s

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Cited By

Rudolph MSchneegass SRizk A(2024)Transcoding V-PCC Point Cloud Streams in Real-timeACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3682062Online publication date: Aug-2024
https://doi.org/10.1145/3682062
Nguyen MAmirpour HTashtarian FTimmerer CHellwagner H(2023)Performance analysis of H2BR: HTTP/2-based segment upgrading to improve the QoE in HASMultimedia Tools and Applications10.1007/s11042-023-15516-583:5(12561-12595)Online publication date: 11-Jul-2023
https://doi.org/10.1007/s11042-023-15516-5
Kiani Mehr SJogalekar PMedhi D(2021)Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clientsJournal of Internet Services and Applications10.1186/s13174-021-00133-y12:1Online publication date: 26-Apr-2021
https://doi.org/10.1186/s13174-021-00133-y
Lorenzi DNguyen MTashtarian FMilani SHellwagner HTimmerer C(2021)Days of future pastProceedings of the 2021 Workshop on Evolution, Performance and Interoperability of QUIC10.1145/3488660.3493802(8-14)Online publication date: 7-Dec-2021
https://dl.acm.org/doi/10.1145/3488660.3493802
Zink MSitaraman RNahrstedt K(2019)Scalable 360° Video Stream Delivery: Challenges, Solutions, and OpportunitiesProceedings of the IEEE10.1109/JPROC.2019.2894817107:4(639-650)Online publication date: Apr-2019
https://doi.org/10.1109/JPROC.2019.2894817
Alt BBallard TSteinmetz RKoeppl HRizk A(2019)CBA: Contextual Quality Adaptation for Adaptive Bitrate Video StreamingIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737418(1000-1008)Online publication date: 29-Apr-2019
https://dl.acm.org/doi/10.1109/INFOCOM.2019.8737418
Bhat DDeshmukh RZink MBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Improving QoE of ABR Streaming Sessions through QUIC RetransmissionsProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240664(1616-1624)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240664
Midoglu CMoulay MMancuso VAlay ÖLutu AGriwodz CCesar PZink MMurray N(2018)Open video datasets over operational mobile networks with MONROEProceedings of the 9th ACM Multimedia Systems Conference10.1145/3204949.3208138(426-431)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3204949.3208138

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