###### Abstract
In this paper, to further leverage the resources near a TaV, we consider four execution modes, namely, Loc execution mode, Loc + SeV execution mode, Loc + Edge execution mode, and Loc + SeV + EdgeV execution mode, where SeV can be a moving or stopped vehicle. When the ratio of tasks executed locally is 100%, the latter three modes become the Loc execution mode. Therefore, the Loc execution mode is listed separately to distinguish. In order to realize the minimization of processing delay and the maximization of accommodated TaVs, we propose a novel partial offloading and adaptive computation resource allocation scheme for VEC-assisted V2X networks by jointly optimizing SeV-selection factors, mode-selection factors, offloading ratios, and computation resource allocation coefficients. The main contributions of this paper are summarized as follows. 1. To maximize the overall benefits of the system, both SeVs and ES are used to provide computation resources via V2V links and V2I links, respectively, in the proposed slow-moving vehicle environment, where four offloading modes are studied, namely, Loc mode, Loc + SeV mode, Loc + SeV + EdgeV mode, and Loc + SeV + EdgeV mode. Based on these offloading modes, the optimal task offloading strategies and resource allocation strategies are realized by selecting SeVs, the offloading strategies development, and the ES computing resources allocation. 2. To reduce the impact of transmission time via V2V links on task processing time, the SeV is selected with the highest channel gain among three CSVs close to a TaV. When the SeV, execution mode, and the proportion of allocated ES computing resources are determined, if and only if the processing time of each execute terminal is the same can they reach the maximization system benefit. Based on this, the expression of the offloading ratio is derived. 3. To allocate computing resources of ES to the TaV that can bring the greatest gain to the system, a potential game based on pre-allocation is proposed, in which the maximum number of iterations is determined first. Then, the best response is used to achieve the best benefit step by step, and the allocated computation resources ratio and offloading strategies can be acquired accordingly. Additionally, the convergence of the proposed game is analyzed. 4. To verify the effectiveness of our scheme, we compare it with four based schemes, i.e., Loc + SeV Execution algorithm (LSVE) [8], Loc + ES Execution algorithm (LESE) [13], Loc + SeV and Loc + ES Execution algorithm (LSVLESE) [19], and Local Execution Algorithm (LEA). The results corroborate the superior performance of our proposed scheme. The remainder of this paper is organized as follows. Section 2 reviews related work. In Section 3, we describe the model and define the relevant functions to formulate the task offloading and computing resources allocation problem. Section 4 introduces a centralized cooperative computation offloading game model, and the BR-CMCRA algorithm is used to find the Nash equilibrium (NE) point. Section 5 evaluates the performance and discusses the numerical results. Finally, Section 6 concludes this paper and discusses future work.