  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 this paper, we focus on studying the economic and strategic
interactions between one content provider (CP) and a large crowd of EDs, where
the EDs can decide whether to cache and share contents for the CP, and the CP
can decide to share a certain revenue with EDs as the incentive of caching and
sharing contents. We formulate such an interaction as a two-stage Stackelberg
game. In Stage I, the CP aims to maximize its own profit by deciding the ratio
of revenue shared with EDs. In Stage II, EDs aim to maximize their own payoffs
by choosing 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
non-atomic game theory. Then, we identify the piece-wise structure and the
unimodal feature of the CP's profit function, based on which we design a
tailored low-complexity one-dimensional search algorithm to achieve the optimal
revenue sharing ratio for the CP in Stage I. 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, comparing with
the Non-MEC system where the CP serves all EDs directly.
