Nowadays, the convergence of mobile edge computing (MEC) and vehicular networks has emerged as a vital enabler 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, the requesting vehicle (RV)-service vehicle (SV) matching scheme and the inter-vehicle collaborative computation are studied, with joint optimization of task offloading decision, communication, and computing resource allocation to minimize energy consumption while satisfying delay requirements. In the Roadside Unit (RSU) Tier, collaboration among RSUs is investigated to further address multi-access issues of uplink subchannels and computing resources for serving unmatched RVs. To tackle this intricate problem, a layered optimization framework is first proposed to obtain task offloading decisions and optimal continuous resource allocation, after which a subchannel allocation scheme is designed to recover the discrete solution with low complexity. Extensive experiments are conducted to demonstrate that the proposed method reduces average energy consumption by at least 15% compared with recent utility maximization and energy cost minimization benchmarks under varying task delay requirements and vehicle scales.

翻译：当前，移动边缘计算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支撑技术。本文面向V2X通信赋能的移动边缘计算车载网络，提出了一种多层任务卸载机制。研究聚焦于包含序列子任务的应用场景，探索两个层级的协同机制。在车辆层，研究了请求车辆与服务车辆的匹配方案及车际协同计算问题，通过联合优化任务卸载决策、通信与计算资源分配，在满足时延约束的前提下最小化能耗。在路边单元层，进一步探究了RSU间的协作机制以解决服务于未匹配请求车辆时面临的上行子信道与计算资源的多接入问题。针对这一复杂优化问题，首先提出分层优化框架以获取任务卸载决策与最优连续资源分配，随后设计子信道分配方案以低复杂度恢复离散解。大量实验表明，在任务时延约束与车辆规模变化的场景下，与近期基于效用最大化及能耗最小化的基准方案相比，所提方法可降低平均能耗至少15%。