In vehicular edge computing environments

the Co-channel interferences (CCI) is a critical problem when edge nodes allocate channels for different data transmission tasks. This article formulates the problem of channel allocation in vehicular edge computing

aiming at allocating sub-channels for different data transmission tasks and maximizing the ratio of successful data transmission. We transform the global optimization problem of channel allocation into a channel allocation potential game

and prove the existence of nash equilibrium. We propose an Incentive-based probability update and strategy selection algorithm

which updates the strategy selection probability according to the incentive value of the selected strategy in each iteration

and further analyzes the Nash equilibrium converge of the algorithm. Finally

we verify the convergence of the proposed algorithm and the effectiveness of the Nash equilibrium. The experimental results show that the proposed algorithm outperforms existing representative algorithms in terms of the ratio of successful data transmission and channel utilization efficiency.