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Joint Optimization Scheme for Caching, Transcoding and Bandwidth in 5G Networks with Mobile Edge Computing

机译:使用移动边缘计算的5G网络缓存,转码和带宽联合优化方案

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Mobile Edge Computing (MEC) and Dynamic Adaptive Streaming over HTTP (DASH) have been considered as two promising technologies in 5G mobile networks. Although some works have been done for MEC and DASH in 5G mobile networks, to the best of our knowledge, these two important issues have traditionally been addressed separately. However, it is necessary to jointly consider these two significant issues to improve the performance of the network and the Quality of Experience of end users. In this paper, we jointly consider MEC and DASH in 5G mobile networks, and propose a joint optimization scheme for caching, transcoding and bandwidth. We first establish a cost model from the perspective of video content provider (VCP), and this cost model consists of caching cost, transcoding cost and bandwidth cost. Our objective is to minimize the total cost of video content provider. Accordingly, we propose a popularity ranking based gradual caching placement algorithm and a cost-efficient transcoding policy, thus optimizing the caching, computing and bandwidth resources and minimizing the total cost. Finally, simulation results show the performance of the proposed scheme.
机译:移动边缘计算(MEC)和HTTP动态自适应流(DASH)已被认为是5G移动网络中的两种有前途的技术。尽管已经为5G移动网络中的MEC和DASH做过一些工作,但据我们所知,这两个重要问题传统上是分别解决的。但是,有必要共同考虑这两个重要问题,以改善网络性能和最终用户的体验质量。在本文中,我们共同考虑了5G移动网络中的MEC和DASH,并提出了针对缓存,代码转换和带宽的联合优化方案。我们首先从视频内容提供商(VCP)的角度建立成本模型,该成本模型包括缓存成本,代码转换成本和带宽成本。我们的目标是最大程度地降低视频内容提供商的总成本。因此,我们提出了一种基于流行度排名的渐进式缓存放置算法和一种具有成本效益的转码策略,从而优化了缓存,计算和带宽资源,并使总成本最小化。最后,仿真结果表明了该方案的性能。

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