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On Optimizing Adaptive Algorithms Based on Rebuffering Probability

Published:28 June 2017Publication History
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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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    • 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

      Copyright © 2017 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 June 2017
      • Accepted: 1 March 2017
      • Revised: 1 January 2017
      • Received: 1 September 2016
      Published in tomm Volume 13, Issue 3s

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