Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. This work aims to optimize resource allocation and task offloading in VEC networks. Specifically, we formulate a game theoretical resource allocation and task offloading problem (GTRATOP) that aims to maximize the system performance by jointly considering the incentive for cooperation, competition among vehicles, heterogeneity between VEC servers and vehicles, and inherent dynamic of VNs. Since the formulated GTRATOP is NP-hard, we propose an adaptive approach for resource allocation and task offloading in VEC networks by incorporating bargaining game and matching game, which is called BARGAIN-MATCH. First, for resource allocation, a bargaining game-based incentive is proposed to stimulate the vehicles and VEC servers to negotiate the optimal resource allocation and pricing decisions. Second, for task offloading, a many-to-one matching scheme is proposed to decide the optimal offloading strategies. Third, the dynamic and time-varying features are considered to adapt the strategies of BARGAIN-MATCH to the real-time VEC networks. Moreover, the BARGAIN-MATCH is proved to be stable and weak Pareto optimal. Simulation results demonstrate that the proposed BARGAIN-MATCH achieves superior system performance and efficiency compared to other methods, especially when the system workload is heavy.

翻译：车辆边缘计算（VEC）通过将云计算资源部署在车辆网络（VNs）边缘，正成为一种前景广阔的车辆网络架构。本文旨在优化VEC网络中的资源分配与任务卸载。具体而言，我们构建了一个博弈论资源分配与任务卸载问题（GTRATOP），通过联合考虑合作激励、车辆间竞争、VEC服务器与车辆的异质性以及车辆网络固有的动态特性，最大化系统性能。由于所构建的GTRATOP为NP-hard问题，我们提出了一种结合议价博弈与匹配博弈的自适应方法（称为BARGAIN-MATCH），用于VEC网络中的资源分配与任务卸载。首先，在资源分配方面，提出一种基于议价博弈的激励方案，促使车辆与VEC服务器协商最优的资源分配与定价决策。其次，在任务卸载方面，提出一种多对一匹配方案，以确定最优卸载策略。再次，考虑动态与时变特性，使BARGAIN-MATCH的策略适应实时VEC网络。此外，证明了BARGAIN-MATCH具有稳定性和弱帕累托最优性。仿真结果表明，与其它方法相比，所提出的BARGAIN-MATCH在系统性能与效率方面表现更优，尤其在系统负载较重时效果更为显著。