  Mobile edge computing (MEC) has emerged for reducing energy consumption and
latency by allowing mobile users to offload computationally intensive tasks to
the MEC server. Due to the spectrum reuse in small cell network, the inter-cell
interference has a great effect on MEC performances. In this paper, for
reducing the energy consumption and latency of MEC, we propose a game theory
based approach to join task offloading decision and resources allocation
together in the MEC system. In this algorithm, the offloading decision, the CPU
capacity adjustment, the transmission power control, and the network
interference management of mobile users are regarded as a game. In this game,
based on the best response strategy, each mobile user makes their own utility
maximum rather than the utility of the whole system. We prove that this game is
an exact potential game and the Nash equilibrium (NE) of this game exists. For
reaching the NE, the best response approach is applied. We calculate the best
response of these three variables. Moreover, we investigate the properties of
this algorithm, including the convergence, the computational complexity, and
the Price of anarchy (PoA). The theoretical analysis shows that the inter-cell
interference affects on the performances of MEC greatly. The NE of this game is
Pareto efficiency. Finally, we evaluate the performances of this algorithm by
simulation. The simulation results illustrate that this algorithm is effective
in improving the performances of the multi-user MEC system.
