ATA: Task-Oriented Adaptive Video Streaming for Cloud-Based Autonomous Driving

With the advancement of mobile transmission and the computational limitations of vehicle terminals, cloud-based autonomous driving has gained significant traction. In order to ensure the quality of video transmission, Adaptive Bitrate (ABR) algorithms are commonly employed. In this paper, we conduct extensive measurements on current ABR algorithms and typical autonomous driving tasks, and we identify two key limitations: (i) existing ABR algorithms commonly apply uniform transmission strategies across diverse driving tasks, and (ii) there is no clear correlation between Quality-of-Experience (QoE) and the performance of autonomous driving tasks, though we reveal specific relationships between them. To overcome these challenges, we propose ATA, a novel task-oriented ABR streaming transmission framework tailored for autonomous driving. ATA enables vehicles to upload streaming optimized for specific autonomous driving task, to optimize the performance of the autonomous driving task in fluctuating network environments. To achieve ATA, we devise a task-oriented ABR strategy, by selecting transmission approaches based on the task requirements. Additionally, we propose an initial bitrate selector, grounded in the Pareto frontier model, to ensure the selection of optimal initial bitrates aligning with current bandwidth conditions. Trace-driven experiments demonstrate that ATA significantly outperforms existing ABR schemes in autonomous driving scenarios. Notably, it achieves improvements in Average Precision (AP) for object detection tasks by 5.06% to 14.25%, reduces Average Orientation Error (AOE) in direction prediction tasks by 7.69% to 15.21%, and enhances overall task performance by 10.27% to 12.55% in multitask scenarios.