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Massive live video distribution using hybrid cellular and ad hoc networks

机译:使用混合蜂窝和临时网络的大规模实时视频分布

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This paper addresses the problem of disseminating multiple live videos to mobile users by using a hybrid cellular and ad hoc network. Specifically, we develop techniques to optimize the overall quality of video delivery by: (a) exploiting the flexibility of layered videos for in-network adaptation to reduce the gap between video coding rate and network capacity, and (b) alleviating the load of individually handling a large number of flows at the cell tower by using device-to-device ad hoc connectivity to deliver videos. We study the problem of optimally choosing the mobile devices that will serve as gateways from the cellular to the ad hoc network, the ad hoc routes from the gateway to individual devices, and the layers to deliver on these ad hoc routes. We develop a Mixed Integer Linear Program (MILP) based solution to the considered problem. We also develop a heuristic algorithm to select the devices, routes, and layers more efficiently than the ideal, but potentially time-consuming MILP-based algorithm. We evaluate the proposed techniques via through simulations. The simulation results show that the proposed algorithms significantly outperform the current solution in terms of overall video quality, transmission latency, delivery ratio, and missed frame ratio. For example, compared to the current cellular network, the MILP-based and the heuristic algorithms result in up to 20 dB higher video quality. Furthermore, the heuristic algorithm runs efficiently yet achieves near-optimal quality: at most 2.3 dB gap across all experiments.
机译:本文通过使用混合蜂窝和临时网络来解决将多个现场视频传播到移动用户的问题。具体而言,我们开发技术以优化视频交付的整体质量:(a)利用网络化适应的分层视频的灵活性,以降低视频编码率和网络容量之间的间隙,(b)减轻单独的负载通过使用设备到设备ad hoc连接来处理蜂窝塔的大量流动,以提供视频。我们研究了最佳地选择将作为从蜂窝到ad hoc网络的网关的移动设备的问题,从网关到各个设备的ad hoc路线,以及在这些ad hoc路由上传递的层。我们将基于混合整数的线性程序(MILP)的解决方案开发给所考虑的问题。我们还开发了一种启发式算法,可以更有效地选择设备,路线和层,而不是理想,但可能耗时的基于MILP的算法。我们通过模拟评估所提出的技术。仿真结果表明,该算法在整体视频质量,传输延迟,输送比率和错过帧比方面显着优于电流解决方案。例如,与当前的蜂窝网络相比,基于MILP的和启发式算法导致高达20 dB的视频质量。此外,启发式算法有效运行,但在所有实验中有效地实现了近乎最佳质量:在所有实验中最多2.3 dB间隙。

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