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Adaptive Streaming in P2P Live Video Systems: A Distributed Rate Control Approach

Published:24 May 2016Publication History
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Abstract

Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard that offers different versions of the same media content to adapt the delivery process over the Internet to dynamic bandwidth fluctuations and different user device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows us to leverage the cooperation among peers, guaranteeing the service of video requests with increased scalability and reduced cost. We propose to combine these two approaches in a P2P-DASH architecture, exploiting the potentiality of both. The new platform is made of several swarms and a different DASH representation is streamed within each of them; unlike client-server DASH architectures, where each client autonomously selects which version to download according to current network conditions and to its device resources, we put forth a new rate control strategy implemented at peer site to maintain a good viewing quality to the local user and to simultaneously guarantee the successful operation of the P2P swarms. The effectiveness of the solution is demonstrated through simulation and it indicates that the P2P-DASH platform is able to provide its users with very good performance, much more satisfying than in a conventional P2P environment where DASH is not employed. Through a comparison with a reference DASH system modeled via the Integer Linear Programming (ILP) approach, the new system is shown to outperform such reference architecture. To further validate the proposal, in terms of both robustness and scalability, system behavior is investigated in the critical condition of a flash crowd, showing that the strong upsurge of new users can be successfully revealed and gradually accommodated.

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          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 12, Issue 3
          June 2016
          227 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/2901366
          Issue’s Table of Contents

          Copyright © 2016 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 24 May 2016
          • Revised: 1 March 2016
          • Accepted: 1 March 2016
          • Received: 1 November 2015
          Published in tomm Volume 12, Issue 3

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