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Joint Sponsor Scheduling in Cellular and Edge Caching Networks for Mobile Video Delivery

机译:移动视频交付的蜂窝和边缘缓存网络中的联合赞助商计划

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The explosive growth of mobile video traffic introduces new challenges for the network infrastructure. Edge caching, as one of the key technologies in 5G wireless networks, has shown great potential to improve the quality of mobile video services by reducing the transmission overhead over backhaul links. With edge caching, content providers (CPs) need to decide not only the traditional data sponsoring strategy on cellular networks (where CPs cover part or all of the mobile users' cellular data cost), but also a novel cache sponsoring strategy on the edge caching networks (where CPs place part of contents on edge networks in advance). In this paper, we study the joint optimization of both sponsors on cellular and edge caching networks for a single CP, aiming at maximizing the CP's revenue. Specifically, we formulate the joint optimization problem as a two-stage sequential decision problem. In stage I, the CP determines the edge caching policy (for a relatively long time period). In stage II, the CP decides the real-time data sponsoring strategy for each content request within the period. We analyze this two-stage decision problem systematically. First, we propose an online sponsoring strategy in stage II based on Lyapunov optimization framework. Then, we propose an edge caching strategy in stage I via predicting the number of aggregate user requests. Simulations on real data traces show that such a joint optimization policy can increase the CP's revenue by 124%-154%, comparing with the traditional data sponsoring policy (i.e., without edge caching). Moreover, the proposed online strategy can achieve 90% of the maximum revenue in the offline benchmark.
机译:移动视频流量的爆炸性增长为网络基础架构带来了新的挑战。边缘缓存作为5G无线网络中的关键技术之一,已显示出巨大的潜力,可通过减少回程链路上的传输开销来提高移动视频服务的质量。通过边缘缓存,内容提供商(CP)不仅需要决定蜂窝网络上的传统数据赞助策略(其中CP可以覆盖部分或全部移动用户的蜂窝数据成本),还需要决定一种新颖的边缘缓存策略网络(CP将内容的一部分预先放置在边缘网络上)。在本文中,我们研究了针对单个CP的蜂窝和边缘缓存网络上的发起人的联合优化,旨在最大化CP的收入。具体来说,我们将联合优化问题表述为两阶段的顺序决策问题。在阶段I中,CP确定边缘缓存策略(在相对较长的时间段内)。在阶段II中,CP决定该时段内每个内容请求的实时数据赞助策略。我们系统地分析了这个两阶段决策问题。首先,我们在第二阶段基于Lyapunov优化框架提出在线赞助策略。然后,我们在第一阶段通过预测聚合用户请求的数量来提出边缘缓存策略。对真实数据轨迹的仿真显示,与传统的数据赞助策略(即不使用边缘缓存)相比,这种联合优化策略可以将CP的收入增加124%-154%。此外,建议的在线策略可以达到离线基准中最大收入的90%。

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