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：车载边缘计算通过将云计算资源部署在车载网络边缘，已成为一种有前景的车载网络架构。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，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VNs的固有动态特性，以最大化系统性能。由于所构建的GTRATOP是NP难题，我们提出了一种融入议价博弈与匹配博弈的自适应VEC网络资源分配与任务卸载方法，称为BARGAIN-MATCH。首先，在资源分配方面，提出基于议价博弈的激励机制，以激励车辆与VEC服务器协商最优资源分配与定价决策。其次，在任务卸载方面，提出多对一匹配方案以确定最优卸载策略。再次，考虑动态与时变特征，使BARGAIN-MATCH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，与其他方法相比，本文提出的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。