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Mobile edge computing (MEC) facilitates computation offloading to edgeserver, as well as task processing via device-to-device (D2D) collaboration.Existing works mainly focus on centralized network-assisted offloadingsolutions, which are unscalable to scenarios involving collaboration amongmassive users. In this paper, we propose a joint framework of decentralized D2Dcollaboration and efficient task offloading for a large-population MEC system.Specifically, we utilize the power of two choices for D2D collaboration, whichenables users to beneficially assist each other in a decentralized manner. Dueto short-range D2D communication and user movements, we formulate a mean fieldmodel on a finite-degree and dynamic graph to analyze the state evolution ofD2D collaboration. We derive the existence, uniqueness and convergence of thestate stationary point so as to provide a tractable collaboration performance.Complementing this D2D collaboration, we further build a Stackelberg game tomodel users' task offloading, where edge server is the leader to determine aservice price, while users are followers to make offloading decisions. Byembedding the Stackelberg game into Lyapunov optimization, we develop an onlineoffloading and pricing scheme, which could optimize server's service utilityand users' system cost simultaneously. Extensive evaluations show that our D2Dcollaboration can mitigate users' workloads by $73.8\%$ and task offloading canachieve high energy efficiency.

Joint D2D Collaboration and Task Offloading for Edge Computing: A Mean Field Graph Approach