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Rate and Power Allocation for Joint Coding and Transmission in Wireless Video Chat Applications

机译:无线视频聊天应用中联合编码和传输的速率和功率分配

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Wireless video chat is a power-consuming and bitrate-intensive application. Unlike video streaming, which is one-way traffic, video chat features distributed two-way traffic relayed via base stations, where resource allocation of a client affects the video quality seen by its communicating partner. In this paper, we study the mechanism design of this application via dynamic pricing, and seek efficiency and fairness of resource utilization . Specifically, we assume that the base station relays video bitstreams and charges a service price on the clients based on the transmission power consumption . Based on the price and a given power budget, the clients allocate bitrate and power for video coding and transmission such that the service price and the distortion seen by their partners are minimized. We study such network dynamics in Stackelberg game-theoretic framework. To solve the problem, we propose a complexity-scalable video encoding method and a power-rate-distortion (PRD) model for video chat. The model is more accurate in describing the PRD characteristics, yet of lower complexity in online updates of its coefficients. Based on the PRD model, we derive the distributed rate and power allocations for the clients. We show that a simple pricing update in the base stations is sufficient for optimal pricing. The proposed algorithms are optimal and converge to the Stackelberg equilibrium. Existing SNR- and power-based pricing schemes could not ensure fairness and efficiency simultaneously. We propose a hybrid pricing scheme that balances these conflicting criteria. Extensive simulations demonstrate superior performance of the proposed methods and solutions.
机译:无线视频聊天是一种耗电且比特率很高的应用程序。与视频流是一种单向流量不同,视频聊天功能是通过基站中继的双向分配流量,客户端的资源分配会影响其通信伙伴看到的视频质量。在本文中,我们通过动态定价研究了该应用程序的机制设计,并寻求资源利用的效率和公平性。具体来说,我们假设基站中继视频比特流,并根据传输功耗在客户端上收取服务价格。客户根据价格和给定的功率预算,为视频编码和传输分配比特率和功率,以使服务价格和合作伙伴看到的失真最小。我们在Stackelberg游戏理论框架中研究此类网络动力学。为了解决该问题,我们提出了一种复杂度可扩展的视频编码方法和一种用于视频聊天的功率率失真(PRD)模型。该模型在描述PRD特征方面更为准确,但在在线更新其系数方面的复杂性较低。基于PRD模型,我们为客户得出分布式速率和功率分配。我们表明,基站中的简单价格更新足以实现最佳价格。所提出的算法是最优的,并且收敛于Stackelberg平衡。现有的基于SNR和功率的定价方案无法同时确保公平性和效率。我们提出了一种混合定价方案,可以平衡这些相互矛盾的标准。大量的仿真证明了所提出的方法和解决方案的优越性能。

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