Minimizing Age of Semantic Information for Analytics-Oriented Video Streaming Systems

Video streaming systems are critical for intelligent applications to transmit video data from end devices to servers for real-time analysis. In contrast to traditional humancentric streaming systems, which prioritize user-perceived metrics, machine-centric streaming systems are designed to continuously provide fresh and accurate information for analytics purposes. Although numerous studies have investigated policies to optimize streaming performance, most of them employ the segment-by-segment streaming framework from human-centric systems. Through comprehensive theoretical analysis and experimentation, we uncover that the segmented streaming approach is sub-optimal for machine-centric streaming systems compared to the straightforward frame-by-frame streaming approach. Furthermore, instead of relying on conventional frame-level metrics, we introduce a novel metric called the Age of Semantic Information (AoSI) to evaluate the performance of analyticsoriented streaming systems. This metric balances the quantity and timeliness of the semantic information. Consequently, we propose a compression ratio adaption method tailored to optimize AoSI performance for frame-by-frame streaming systems. This method leverages a deep learning (DL)-based predictor to discover the dynamic, latent relationships between compression and inference accuracy. Evaluated on actual streaming prototypes and real-world datasets, our method significantly surpasses both segmented and frame-by-frame baseline methods in terms of worst-case and average AoSI performance.