Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing

Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision making of different stakeholders in the ecosystem. In this article, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the CP designs the incentive scheme for EDs to cache and share contents, and EDs decide whether to cache and share contents for the CP. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue (as incentives) shared with EDs who choose to cache and share contents, aiming at maximizing its own profit. In Stage II, EDs choose to be agents who cache and share contents, and meanwhile gain a certain revenue from the CP, or requesters who do not cache but request contents in the on-demand fashion. We first analyze the EDs' best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the nonatomic game theory. Then, we identify the piecewise structure and the unimodal feature of the CP's profit function, based on which we design a tailored low-complexity 1-D search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. The simulation results show that both the CP's profit and the EDs' total welfare can be improved significantly (e.g., by 120% and 50%, respectively,) by using the proposed Crowd-MECS system, comparing with the non-MEC system where the CP serves all EDs directly.