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ABMA+: lightweight and efficient algorithm for HTTP adaptive streaming

Published: 10 May 2016 Publication History

Abstract

Currently exploited adaptive video streaming algorithms mainly focus on maximizing video representation while they usually not assess the risk of video freezing and not care about the influence of video representation switching. We propose a new Adaptation & Buffer Management Algorithm, called ABMA+, which selects video representation based on the predicted probability of video freezing. The algorithm continuously estimates segment download time characteristics and use the pre-computed playout buffer map to select the maximum video representation which guarantee smooth content playout. Our algorithm, thanks to the buffer map, avoids heavy online computation so it could be widely deployed on different terminals. The performed simulation and trial experiments confirm the efficiency of our approach.

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  • (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)Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth dropsEAI Endorsed Transactions on Industrial Networks and Intelligent Systems10.4108/eetinis.v10i2.299410:2(e3)Online publication date: 9-Jun-2023
  • (2023)Deep Reinforcement Learning-Based Approach for Video Streaming: Dynamic Adaptive Video Streaming over HTTPApplied Sciences10.3390/app13211169713:21(11697)Online publication date: 26-Oct-2023
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    cover image ACM Conferences
    MMSys '16: Proceedings of the 7th International Conference on Multimedia Systems
    May 2016
    420 pages
    ISBN:9781450342971
    DOI:10.1145/2910017
    • General Chair:
    • Christian Timmerer
    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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    Publication History

    Published: 10 May 2016

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    Author Tags

    1. MPEG DASH
    2. adaptive video streaming
    3. modelling
    4. performance evaluation

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    • Research-article

    Funding Sources

    • DISEDAN Project - European CHIST-ERA Program

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    MMSys'16
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    MMSys'16: Multimedia Systems Conference 2016
    May 10 - 13, 2016
    Klagenfurt, Austria

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    MMSys '16 Paper Acceptance Rate 20 of 71 submissions, 28%;
    Overall Acceptance Rate 176 of 530 submissions, 33%

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    Cited By

    View all
    • (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)Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth dropsEAI Endorsed Transactions on Industrial Networks and Intelligent Systems10.4108/eetinis.v10i2.299410:2(e3)Online publication date: 9-Jun-2023
    • (2023)Deep Reinforcement Learning-Based Approach for Video Streaming: Dynamic Adaptive Video Streaming over HTTPApplied Sciences10.3390/app13211169713:21(11697)Online publication date: 26-Oct-2023
    • (2023)Timely and Accurate Bitrate Switching in HTTP Adaptive Streaming With Date-Driven I-Frame PredictionIEEE Transactions on Multimedia10.1109/TMM.2022.316538125(3753-3762)Online publication date: 2023
    • (2023)TPMI:Accurate Throughput Prediction for Better Bitrate Selection in Adaptive Video Streaming2023 2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies (SMC-IoT)10.1109/SMC-IoT62253.2023.00033(140-145)Online publication date: 29-Dec-2023
    • (2023)HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistanceJournal of Network and Computer Applications10.1016/j.jnca.2023.103604213(103604)Online publication date: Apr-2023
    • (2022)Performance Analysis of an Adaptive Rate Scheme for QoE-Assured Mobile VR Video StreamingComputers10.3390/computers1105006911:5(69)Online publication date: 29-Apr-2022
    • (2022)Machine Learning for Computer Systems and Networking: A SurveyACM Computing Surveys10.1145/352305755:4(1-36)Online publication date: 21-Nov-2022
    • (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
    • (2022)An Elastic DASH-based Bitrate Adaptation Scheme for Smooth On-Demand Video Streaming2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)10.1109/BMSB55706.2022.9828754(1-6)Online publication date: 15-Jun-2022
    • Show More Cited By

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