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Online Learning for Adaptive Video Streaming in Mobile Networks

Published:27 January 2022Publication History
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Abstract

In this paper, we propose a novel algorithm for video bitrate adaptation in HTTP Adaptive Streaming (HAS), based on online learning. The proposed algorithm, named Learn2Adapt (L2A), is shown to provide a robust bitrate adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, or application-specific adjustments. These properties make it very suitable for mobile users, who typically experience fast variations in channel characteristics. Experimental results, over real 4G traffic traces, show that L2A improves on the overall Quality of Experience (QoE) and in particular the average streaming bitrate, a result obtained independently of the channel and application scenarios.

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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 18, Issue 1
      January 2022
      517 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3505205
      Issue’s Table of Contents

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      Publication History

      • Published: 27 January 2022
      • Accepted: 1 April 2021
      • Revised: 1 February 2021
      • Received: 1 July 2020
      Published in tomm Volume 18, Issue 1

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