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A game theoretic framework for optimal resource allocation in P2P scalable video streaming

机译:P2P可伸缩视频流中用于优化资源分配的游戏理论框架

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In this paper we describe a game theoretic framework for scalable video streaming over a peer-to-peer network. The proposed system integrates optimal resource allocation functionalities with an incentive provision mechanism for data sharing. First of all, we introduce an algorithm for packet scheduling that allows users to download a specific sub-set of the original scalable bit-stream, depending on the current network conditions. Furthermore, we present an algorithm that aims both at identifying free-riders and minimising transmission delay. Uncooperative peers are cut out of this system, while users upload more data to those which have less to share, in order to fully exploit the resources of all the peers. Experimental evaluation shows that this model can effectively cope with free-riders and minimise transmission delay for scalable video streaming.
机译:在本文中,我们描述了一种用于在对等网络上扩展视频流的游戏理论框架。所提出的系统将最佳资源分配功能与用于数据共享的激励提供机制集成在一起。首先,我们介绍一种用于数据包调度的算法,该算法允许用户根据当前网络条件下载原始可伸缩比特流的特定子集。此外,我们提出了一种旨在识别搭便车并使传输延迟最小的算法。不合作的对等方从该系统中删除,而用户将更多的数据上传到共享较少的数据上,以便充分利用所有对等方的资源。实验评估表明,该模型可以有效应对搭便车,并最大程度地减少可扩展视频流的传输延迟。

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