Nowadays, the convergence of Mobile Edge Computing (MEC) and vehicular networks has emerged as a vital facilitator for the ever-increasing intelligent onboard applications. This paper proposes a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores the collaboration of two tiers. In the vehicle tier, we design a needing vehicle (NV)-helping vehicle (HV) matching scheme and inter-vehicle collaborative computation is studied, with joint optimization of task offloading decision, communication, and computation resource allocation to minimize energy consumption and meet delay requirements. In the roadside unit (RSU) tier, collaboration among RSUs is investigated to further address multi-access issues of subchannel and computation resources for multiple vehicles. A two-step method is designed to first obtain optimal continuous solutions of multifaceted variables, and then derive the solution for discrete uplink subchannel allocation with low complexity. Detailed experiments are conducted to demonstrate the proposed method reduces average energy consumption by at least 15% compared with benchmarks under varying task delay requirements and numbers of vehicles and assess the impact of various parameters on system energy consumption.

翻译：如今，移动边缘计算（MEC）与车载网络的融合已成为日益增长的智能车载应用的关键使能技术。本文提出一种利用车联网（V2X）通信的MEC赋能车载网络多层任务卸载机制。研究聚焦具有顺序子任务的应用场景，探索两个层级的协同机制。在车辆层，我们设计了需求车辆（NV）-协助车辆（HV）匹配方案，研究车辆间协同计算，通过联合优化任务卸载决策、通信与计算资源分配，以降低能耗并满足时延要求。在路侧单元（RSU）层，通过研究RSU间协作进一步解决多车辆的子信道与计算资源多接入问题。设计了一种两步求解方法：首先获得多维连续变量的最优解，进而以较低复杂度推导离散上行子信道分配方案。详尽的实验表明，在不同任务时延要求及车辆数量条件下，所提方法较基准方案平均能耗降低至少15%，并评估了各类参数对系统能耗的影响。