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Feedback control for adaptive live video streaming

Published: 23 February 2011 Publication History

Abstract

Multimedia content feeds an ever increasing fraction of the Internet traffic. Video streaming is one of the most important applications driving this trend. Adaptive video streaming is a relevant advancement with respect to classic progressive download streaming such as the one employed by YouTube. It consists in dynamically adapting the content bitrate in order to provide the maximum Quality of Experience, given the current available bandwidth, while ensuring a continuous reproduction. In this paper we propose a Quality Adaptation Controller (QAC) for live adaptive video streaming designed by employing feedback control theory. An experimental comparison with Akamai adaptive video streaming has been carried out. We have found the following main results: 1) QAC is able to throttle the video quality to match the available bandwidth with a transient of less than 30s while ensuring a continuous video reproduction; 2) QAC fairly shares the available bandwidth both in the cases of a concurrent TCP greedy connection or a concurrent video streaming flow; 3) Akamai underutilizes the available bandwidth due to the conservativeness of its heuristic algorithm; moreover, when abrupt available bandwidth reductions occur, the video reproduction is affected by interruptions.

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References

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Move Networks HD adaptive video streaming. http://www.movenetworkshd.com.
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Adobe Systems Inc. Real-Time Messaging Protocol (RTMP) Specification. 2009.
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L. De Cicco and S. Mascolo. An Experimental Investigation of the Akamai Adaptive Video Streaming. In Proc. of USAB 2010, Nov. 4-5, 2010.
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R. Kuschnig, I. Kofler, and H. Hellwagner. An evaluation of TCP-based rate-control algorithms for adaptive internet streaming of H. 264/SVC. In Proc. of ACM SIGMM conference on Multimedia systems, pages 157--168, 2010.
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R. Pantos and W. May. HTTP Live Streaming. IETF Draft, June 2010.
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B. Wang, J. Kurose, P. Shenoy, and D. Towsley. Multimedia streaming via TCP: An analytic performance study. ACM TOMCCAP, 4(2):1--22, 2008.
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Cited By

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  • (2024)C2: ABR Streaming in Cognizant of Consumption Context for Improved QoE and Resource Usage TradeoffsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365251720:9(1-27)Online publication date: 16-Aug-2024
  • (2024)Under Control: A Control Theory Introduction for Computer Scientists2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOS-C63493.2024.00044(130-139)Online publication date: 16-Sep-2024
  • (2024)A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and OpportunitiesIEEE Access10.1109/ACCESS.2024.349161312(170803-170839)Online publication date: 2024
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Published In

cover image ACM Conferences
MMSys '11: Proceedings of the second annual ACM conference on Multimedia systems
February 2011
294 pages
ISBN:9781450305181
DOI:10.1145/1943552
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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Publication History

Published: 23 February 2011

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Author Tags

  1. adaptive video streaming
  2. quality adaptation controller
  3. quality feedback control

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MMSYS '11
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MMSYS '11: MMSYS '11 - Multimedia Systems Conference
February 23 - 25, 2011
CA, San Jose, USA

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Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

View all
  • (2024)C2: ABR Streaming in Cognizant of Consumption Context for Improved QoE and Resource Usage TradeoffsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365251720:9(1-27)Online publication date: 16-Aug-2024
  • (2024)Under Control: A Control Theory Introduction for Computer Scientists2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOS-C63493.2024.00044(130-139)Online publication date: 16-Sep-2024
  • (2024)A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and OpportunitiesIEEE Access10.1109/ACCESS.2024.349161312(170803-170839)Online publication date: 2024
  • (2023)MetaStream: Live Volumetric Content Capture, Creation, Delivery, and Rendering in Real TimeProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592530(1-15)Online publication date: 2-Oct-2023
  • (2023)A Quality-of-Experience Database for Adaptive Omnidirectional Video StreamingIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2023.330052917:5(949-963)Online publication date: Sep-2023
  • (2023)Reinforcement learning-based rate adaptation in dynamic video streamingTelecommunication Systems10.1007/s11235-023-01031-383:4(395-407)Online publication date: 13-Jun-2023
  • (2023)Content-aware QoE optimization in MEC-assisted Mobile video streamingMultimedia Tools and Applications10.1007/s11042-023-15163-w82:27(42053-42085)Online publication date: 4-Apr-2023
  • (2022)Prism: Handling Packet Loss for Ultra-low Latency VideoProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3547856(3104-3114)Online publication date: 10-Oct-2022
  • (2022)Cratus: A Lightweight and Robust Approach for Mobile Live StreamingIEEE Transactions on Mobile Computing10.1109/TMC.2020.304882621:8(2761-2775)Online publication date: 1-Aug-2022
  • (2021)Server-Side Segment Selection for Low-Latency Streaming—S4SSMPTE Motion Imaging Journal10.5594/JMI.2021.3117125130:10(50-56)Online publication date: Nov-2021
  • Show More Cited By

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