摘要:移动用户间日益普及的视频数据传输，使得传统无线通信网络上的流量爆炸性增长问题变得愈发严重，而基于D2D通信的数据机会传输被认为是能够实现数据卸载的有效方法.然而在移动机会网络中，数据传输主要通过数据复制和机会转发而实现.为了获得较高的投递率和较低的投递时延，数据复制往往被过度使用，冗余的数据包不仅会消耗大量的设备和网络资源，还会增加网络的传输负载，降低网络性能.对于视频数据传输，由于其较强的持续性和远高于一般标量数据的数据量，该问题会更加突出.因此，基于多人协作博弈，提出了一种面向视频传输质量的机会路由算法VOR-MG，实现对视频传输质量和传输开销的优化.具体来讲，首先建立视频数据包的边缘质量增益模型，然后把多用户相遇时的视频数据传输建模为一个多用户协作博弈，最后，基于其纳什最优解来对每个视频数据包的复制或者转发进行决策，从而保证在视频传输质量最优的情况下，使得网络中的视频数据包备份个数最少.基于合成数据集和真实数据集的仿真结果表明：其视频传输质量比对比算法高出1 dB~2dB，但是平均最大数据包备份数仅为同类算法的25%左右.



Abstract:The increasing popularity of video delivery among mobile users makes the problem of explosive traffic growth becoming more and more serious for traditional wireless networks, and video transmission based on D2D (device-to-device) communication through MONs (mobile opportunistic networks) is regarded as an ideal way to resolve this issue. However, data transmission in MONs is mainly through the two ways: data replication and data forwarding. Thus, to achieve high delivery ratio and low delivery delay, data replication is usually excessively exploited, and the large number of redundant replicas will not only consume large amount of nodal resource but also greatly increase the overload of networks. For video transmission, this issue becomes more severe for its volume and continuity. Thus, this study proposes a novel routing scheme for video data transmission in MONs, based on multi-player cooperative game, which can maximize the quality of reconstructed video data while minimizing the overhead of nodal and network resources. Specifically, the marginal gain model is first constructed for video delivery quality, and then the video data transmission is modeled among multiple encounters as a multi-player cooperative game. Under the guidance of Nash equilibrium theory, the video data carried by these encountering nodes is adaptively and optimally re-assigned among them. Extensive simulations based on real-life mobility traces and synthetic traces have validated the effectiveness of the proposed routing algorithm.



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