AG-CoDiff: Aerial–Ground Cooperative Diffusion System for Enhancing AIGC Services in Vehicular Networks

Abstract

The advancement of Internet of Vehicles (IoV) has led to significant progress in integrating Artificial Intelligence Generated Content (AIGC) into vehicular networks. However, current research predominantly focuses on traditional performance metrics such as latency and energy consumption, while overlooking the joint optimization of semantic consistency, image quality, and communication metrics, which affect the QoS of users directly. In this paper, we propose a novel Aerial-Ground Cooperative Diffusion (AG-CoDiff) system in an Uncrewed Aerial Vehicle (UAV)-assisted IoV, which integrates Collaborative Fine-Tuning (CoFT) and Collaborative Inference (CoInf) for a complex scene understanding in autonomous driving. In the CoFT phase, vehicles capture image data and transmit it to UAVs for filtering and fine-tuning, while in the CoInf phase, UAVs perform shared inference, and vehicles conduct local inference based on received intermediate results. A novel satisfaction metric is introduced to balance inference latency, content quality, and energy consumption simultaneously. Additionally, a dynamic resource allocation algorithm based on Twin Delayed Deep Deterministic Policy Gradient (TD3) and an inference partitioning strategy using the Dream Optimization Algorithm (DOA) are proposed. Experimental results validate the effectiveness of the proposed system. In the CoFT phase, the proposed scheme improves the image quality score by 82.87% and 104.55% over the random-cooperative and non-cooperative baselines, respectively, and improves CLIPScore by 2.29% and 7.50%. In the CoInf phase, the DOA-based partitioning strategy achieves 15.33%–33.64% improvement in objective value over competing heuristics, demonstrating the effectiveness of AG-CoDiff in jointly balancing latency, generation quality, and energy consumption.