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SMDP-based Resource Allocation for Video Streaming in Cognitive Vehicular Networks

机译:基于SMDP的资源分配在认知车辆网络中视频流的资源分配

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Video streaming over vehicular network is playing a more and more important role in our daily life. In this paper, considering a cognitive radio-enabled roadside infrastructure network, which is potentially of flexible spectrum, a joint call admission control and channel allocation scheme is proposed to facilitate the transmission of scalable video coding (SVC)-based video service for vehicle users. To capture the dynamic of vehicle users' mobility and the change of cognitive bands' availability, we formulate the joint resource allocation problem as a semi-Markov decision process (SMDP). Specifically, taking into account the system state consisting of the spatial distribution of vehicle users with different numbers of video layers and the availability of the cognitive bands, the optimal resource allocation is addressed to maximize a long-term reward, which aims at striking a good balance between the two key aspects of video quality, namely, high peak signal-to-noise ratio (PSNR) and smooth playback. Simulation results show the effectiveness of the proposed scheme in terms of improving both aforesaid aspects of video quality for vehicle users in a vehicular network.
机译:视频流过车辆网络在我们日常生活中发挥了越来越重要的作用。在本文中,考虑到具有灵活频谱的认知无线电路边基础设施网络,提出了一个关节呼叫准入控制和信道分配方案,以便于传输用于车辆用户的可伸缩视频编码(SVC)的视频服务。为了捕捉车辆用户的动态和认知乐队的变化的可用性,我们将联合资源分配问题作为半马尔可夫决策过程(SMDP)。具体而言,考虑到系统状态,由具有不同数量的视频层的车辆用户的空间分布以及认知乐队的可用性组成的系统状态,解决了最佳资源分配,以最大化长期奖励,这旨在引人注目的奖励视频质量的两个关键方面之间的平衡,即高峰信噪比(PSNR)和播放平滑。仿真结果表明了所提出的方案在提高车辆网络中的车辆用户的视频质量方面的效果。

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