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