Crowdsourcing for Mobile Edge Caching: A Game-Theoretic Analysis

Abstract

Mobile crowdsourced edge caching is emerging as a promising caching paradigm by crowdsourcing the storage resources of massive edge devices (EDs) for content caching. The successful technology adoption and commercial deployment rely on a comprehensive understanding of the economic interactions among different network entities involved in such a system. In this paper, we focus on the economic interactions between one content provider (CP) and a large number of EDs, where the CP shares a certain revenue with EDs as the incentive of caching contents, and EDs decide whether to cache contents and share the cached contents with others. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue shared with EDs, aiming at maximizing its own profit. In Stage II, each ED chooses to be an agent who caches contents and shares the cached contents with other EDs, or a requester who does not cache but requests contents from agents. e first analyze the existence and uniqueness of the Stage II subgame equilibrium by using the evolutionary game theory. Then, we identify the piece-wise structure of the CP's profit function, and derive the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that a higher revenue sharing ratio for EDs or a larger serving capacity of EDs can drive more EDs to choose to be agents and meanwhile achieve a higher total welfare for EDs at the equilibrium. Moreover, a larger content price of the CP will lead to a larger welfare loss for EDs.