Pricing in the Open Market of Crowdsourced Video Edge Caching: A Newcomer Perspective

By placing popular contents on the network edges, edge caching becomes a promising technique to improve the quality of experience (QoE) of the end users and reduce backhaul link congestion. In this paper, we examine an open market of crowdsourced video edge caching, where within each time slot, the newcome private edge devices strategically declare their own bids to the Video Content Provider (VCP) operator for contributions; and the operator optimally recruits caching devices among the newcome and existing served devices to maximize the expected QoE, under a budget constraint. From the perspective of newcome edge devices, we propose and study a novel pricing problem, namely Pri-CVEC, to determine the bid prices for profit maximization. The problem is challenging due to the importing of strategic interactions between the newcome devices and the VCP operator, and competition between the newcome and the existing served devices. We formulate it as a stackelberg knapsack problem. By leveraging the dynamic programming and linear programming-relaxation method, we propose Pri-DP and PriLPR algorithm, respectively. We extensively conduct simulation experiments to verify the advantages of our approaches.