Multimedia Tasks-Oriented Edge Computing Offloading Scheme Based on Graph Neural Network in Vehicular Networks

With the advancement of vehicular networking technologies, in-vehicle devices are increasingly involved in complex computational tasks, posing new challenges to the vehicles’ computational capabilities and energy consumption. This study addresses the complexities associated with task deployment by proposing a novel integrated framework that synergizes the strengths of deep reinforcement learning (DRL) and graph neural networks (GNNs). The proposed framework leverages the relational capabilities of GNNs to capture inter-task dependencies while utilizing the adaptive learning capabilities of DRL to optimize task offloading decisions in real time. First, we propose a GNN-based task offloading scheme that utilizes graph structures to represent task dependencies and optimizes task deployment through graph neural networks. Second, deep Reinforcement Learning (DRL) is introduced to learn optimal task deployment policies, enhancing the efficiency and accuracy of task deployment. Finally, the performance of the proposed scheme is validated through simulation experiments, including metrics such as model stability, subtask deployment error rate, expected energy consumption, and algorithm solution time. The proposed hybrid approach not only enhances the efficiency of resource allocation but also minimizes processing delays, ultimately contributing to improved performance in vehicular networks.