Cross-Layer QoE-Based Incentive Mechanism for Video Streaming in Multi-Hop Wireless Networks

Mahdi Mousavi, Hussein Al-Shatri, Wasiur R. KhudaBukhsh, H. Koeppl, A. Klein
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引用次数: 4

Abstract

We study video dissemination in a multi-hop wireless network with a source and several users. The source intends to stream a video to the users of the network. For the sake of energy-efficiency, the video is disseminated through the whole network by the help of some users that forward the video to who other users. In such networks, designing a proper incentive for the forwarding users who consume energy for forwarding the video to others is of high importance. In this paper, we design an incentive mechanism based on a game-theoretic model in which a user is paid by its receiving users in case of forwarding the video to them. The video is layered and a higher quality of experience (QoE) at a receiving user is possible by receiving more layers of the video. A utility function is proposed for every user that captures the perceived QoE at the user and the cost she pays for the video. Moreover, it captures the reward the user receives from others in exchange for forwarding the video to them. The utility function is designed in a way that the users who contribute more in the network, in terms of forwarding the video to others, are paid more. A non-cooperative game is formulated in which every user selfishly maximizes its own utility and determines the number of video layers she prefers to receive. The game is iterative and converges to the Nash equilibrium point. The simulation results demonstrate that the proposed game theoretic model results in a higher QoE at the users as compared to that of a non-incentive video dissemination model.
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多跳无线网络中基于跨层qos的视频流激励机制
研究了多跳多源多用户无线网络中的视频传播问题。该源打算向网络用户流式传输视频。出于节能的考虑,视频通过一些用户的帮助在全网传播,这些用户将视频转发给其他用户。在这样的网络中,为转发用户在转发视频时消耗的能量设计适当的激励机制是非常重要的。本文设计了一种基于博弈论模型的激励机制,即当用户转发视频给接收用户时,用户由接收用户付费。视频是分层的,接收用户可以通过接收更多的视频层来获得更高的体验质量(QoE)。提出了一个实用函数,用于捕获用户的感知QoE和用户为视频支付的成本。此外,它还捕获用户从其他人那里获得的奖励,以换取将视频转发给他们。效用函数的设计方式是,在网络中贡献更多的用户,在转发视频给其他人方面,获得更多的报酬。在非合作游戏中,每个用户都自私地最大化自己的效用,并决定自己喜欢接收的视频层数。该博弈具有迭代性,收敛于纳什均衡点。仿真结果表明,与非激励视频传播模型相比,本文提出的博弈论模型对用户产生了更高的QoE。
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