SpotStream: Real-Time Video Transmission for Autonomous Driving via Small Object-Aware ROI

摘要

Cloud-based autonomous driving relies on low-latency video transmission for accurate downstream perception tasks. However, low-latency video streaming solutions, optimized for human Quality of Experience (QoE), are misaligned with the needs of machine vision tasks. While Region-of-Interest (ROI) encoding aims to bridge this gap, existing methods suffer from two critical limitations: first, insufficient protection for small and vulnerable objects, which are often missed by detection mechanisms or given uniform, inadequate resource allocation; second, prohibitive encoding overhead from fine-grained partitioning, leading to poor system robustness in dynamic network environments. To address these issues, we propose SpotStream, a novel low-latency video transmission framework. SpotStream features a dual-stream importance prediction network to ensure comprehensive coverage and prioritized protection for small objects. It couples this with an efficient, multi-level ROI assignment strategy that significantly reduces encoding overhead by simplifying region structure while focusing resources on critical targets. Comprehensive evaluations demonstrate that SpotStream substantially outperforms state-of-the-art baselines. Notably, under challenging real-world network traces, Spot-Stream improves the F1 score for critical small objects by an average of 12.9% while maintaining excellent system robustness.