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On Optimizing Adaptive Algorithms Based on Rebuffering Probability

Published: 28 June 2017 Publication History

Abstract

Traditionally, video adaptive algorithms aim to select the representation that better fits to the current download rate. In recent years, a number of new approaches appeared that take into account the buffer occupancy and the probability of video rebuffering as important indicators of the representation to be selected. We propose an optimization of the existing algorithm based on rebuffering probability and argue that the algorithm should avoid the situations when the client buffer is full and the download is stopped, since these situations decrease the efficiency of the algorithm. Reducing full buffer states does not increase the rebuffering probability thanks to a clever management of the client buffer, which analyses the buffer occupancy and downloads higher bitrate representations only in the case of high buffer occupancy.

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Cited By

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  • (2023)HTBT: A Hybrid DASH Adaptation Algorithm Using Takagi-Sugeno-Kang Fuzzy ModelAdvances in Electrical and Computer Engineering10.4316/AECE.2023.0100123:1(3-10)Online publication date: 2023
  • (2022)An encoding-aware bitrate adaptation mechanism for video streaming over HTTPMultimedia Tools and Applications10.1007/s11042-022-12520-z81:19(27423-27451)Online publication date: 1-Aug-2022
  • (2021)Quality Evaluation of Online Mental Health Education Based on Reinforcement Learning in the PandemicDiscrete Dynamics in Nature and Society10.1155/2021/78491942021(1-12)Online publication date: 17-Dec-2021

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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
    Issue’s Table of Contents
    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: 28 June 2017
    Accepted: 01 March 2017
    Revised: 01 January 2017
    Received: 01 September 2016
    Published in TOMM Volume 13, Issue 3s

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    Author Tags

    1. Adaptive streaming
    2. ERA
    3. adaptation algorithms

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    • Research-article
    • Research
    • Refereed

    Funding Sources

    • Celtic-Plus Programme
    • European Commission and the Narodowe Centrum Badań i Rozwoju in Poland
    • MONALIS:monitoring and control of QoE in large-scale media distribution architectures

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    Cited By

    View all
    • (2023)HTBT: A Hybrid DASH Adaptation Algorithm Using Takagi-Sugeno-Kang Fuzzy ModelAdvances in Electrical and Computer Engineering10.4316/AECE.2023.0100123:1(3-10)Online publication date: 2023
    • (2022)An encoding-aware bitrate adaptation mechanism for video streaming over HTTPMultimedia Tools and Applications10.1007/s11042-022-12520-z81:19(27423-27451)Online publication date: 1-Aug-2022
    • (2021)Quality Evaluation of Online Mental Health Education Based on Reinforcement Learning in the PandemicDiscrete Dynamics in Nature and Society10.1155/2021/78491942021(1-12)Online publication date: 17-Dec-2021

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