Joint Pricing and Cache Placement for Video Caching: A Game Theoretic Approach
Joint Pricing and Cache Placement for Video Caching: A Game Theoretic Approach
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视频缓存的联合定价和缓存放置：一种博弈论方法
DOI：
10.1109/jsac.2019.2916279
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发表时间：
2019-07-01
影响因子：
16.4
通讯作者：
Steinbach, Eckehard
中科院分区：
文献类型：
--
作者：
Zou, Junni;Li, Chenglin;Steinbach, Eckehard
在移动的视频传输中，缓存技术可以有效地平滑业务的时间变化，减少冗余数据传输。在本文中，我们考虑一个视频缓存系统组成的视频提供商（VP），一个移动的网络运营商（MNO）与一组缓存启用的基站（BS），和多个移动的用户。VP将一些流行的视频出租给MNO，而MNO将这些出租的视频放置在其BS的本地缓存中以节省昂贵的回程传输成本。然而，在这样一个双边市场中，这两个实体由于对视频定价的相反预期而相互竞争以获得各自的利润。为了解决这个问题，我们使用Stackelberg游戏的框架来模拟两个实体之间的竞争，并提出了一个联合视频定价和缓存放置策略，考虑到视频文件大小的异质性，并利用经济学领域的经典需求定律。所提出的优化问题是能够共同最大化的VP和MNO的利润的视频定价和该高速缓存放置策略的最佳选择给定的价格，为非合作BS缓存和合作BS缓存的情况下。然后，我们开发迭代算法的基础上，动态规划和梯度上升，分别为这两种情况下找到Stackelberg均衡（SE）。仿真结果进一步表明，所提出的联合优化公式遵循经济学中的需求规律，并且针对这两种情况的所提出的算法可以有效地收敛到联合最大化VP和MNO的利润的SE点。
Caching can effectively smooth the temporal traffic variability and decrease the redundant data transmission in mobile video delivery. In this paper, we consider a video caching system consisting of a video provider (VP), a mobile network operator (MNO) with a set of cache-enabled base stations (BSs), and multiple mobile users. The VP leases some popular videos to the MNO, while the MNO places these rented videos in local caches of its BSs to save expensive backhaul transmission cost. However, in such a two-sided market, these two entities are competing with each other for their own profit due to their opposite expectation on the video pricing. To address this, we model the competition between the two entities using the framework of Stackelberg games and propose a joint video pricing and cache placement strategy by considering the heterogeneity of video file sizes and exploiting the classic law of demand from the field of economics. The proposed optimization problem is able to jointly maximize the profit of the VP and the MNO by the optimal selection of the video pricing and the cache placement strategy given that price, for both noncooperative BS caching and cooperative BS caching cases. We then develop iterative algorithms based on dynamic programming and gradient ascent, respectively, for these two cases to find the Stackelberg equilibrium (SE). The simulation results further show that the proposed joint optimization formulation follows the law of demand in economics, and the proposed algorithms for both cases can efficiently converge to the SE point that jointly maximizes the profit for both the VP and the MNO.