Multi-access edge computing (MEC) technology is envisioned as a promising paradigm to achieve the user needs of low-latency applications. Complex computation tasks are offloaded from resource-constrained devices to resource-rich devices to reduce task completion delays. However, given the high-speed mobility of vehicles, the traditional MEC network architecture is insufficient for the Internet of Vehicle (IoV) computing scenarios. Besides, many resource-rich idle vehicles can be used as mobile servers to perform computing tasks, extending the choices for vehicle users. To better integrate MEC and IoV technologies, we introduce a Cellular-V2X-based MEC offloading scenario, enabling MEC servers and vehicle nodes in the 5G cellular network environment to perform offloading cooperatively. For a typical delay-sensitive task, taking the Augmented Reality (AR) application as an example, we divide the task into multiple subtasks with linear correlation. A mobility-aware dynamic offloading algorithm (MADO) is proposed to minimize the impact of vehicular mobility on offloading. The algorithm is used to find an optimal resource allocation, which reduces the overhead of the entire offloading process. It continuously updates the resource selection and the offloading strategy with the vehicular location changes. Simulation results corroborate that our proposed MADO algorithm can effectively reduce task completion time, improve task completion success rate and adapt to a dynamic environment.

多路访问边缘计算 (MEC) 技术被设想为一种有前途的范例，可以满足用户对低延迟应用程序的需求。复杂的计算任务从资源受限的设备卸载到资源丰富的设备，以减少任务完成延迟。然而，考虑到车辆的高速移动性，传统的 MEC 网络架构不足以满足车联网 (IoV) 计算场景。此外，许多资源丰富的闲置车辆可以作为移动服务器执行计算任务，扩大了车辆用户的选择范围。为了更好地融合 MEC 和车联网技术，我们引入了基于 Cellular-V2X 的 MEC 卸载场景，使 5G 蜂窝网络环境中的 MEC 服务器和车辆节点能够协同执行卸载。对于典型的延迟敏感任务，以增强现实（AR）应用为例，我们将任务划分为多个线性相关的子任务。提出了一种移动感知动态卸载算法（MADO）以最小化车辆移动性对卸载的影响。该算法用于寻找最佳资源分配，从而减少整个卸载过程的开销。它随着车辆位置的变化不断更新资源选择和卸载策略。仿真结果证实，我们提出的MADO算法可以有效减少任务完成时间，提高任务完成成功率，适应动态环境。提出了一种移动感知动态卸载算法（MADO）以最小化车辆移动性对卸载的影响。该算法用于寻找最佳资源分配，从而减少整个卸载过程的开销。它随着车辆位置的变化不断更新资源选择和卸载策略。仿真结果证实，我们提出的MADO算法可以有效减少任务完成时间，提高任务完成成功率，适应动态环境。提出了一种移动感知动态卸载算法（MADO）以最小化车辆移动性对卸载的影响。该算法用于寻找最佳资源分配，从而减少整个卸载过程的开销。它随着车辆位置的变化不断更新资源选择和卸载策略。仿真结果证实，我们提出的MADO算法可以有效减少任务完成时间，提高任务完成成功率，适应动态环境。