RIVA: Communication-Efficient Streaming Control for Real-time Industrial Video Analytics

Real-time industrial video analytics is widely applied across diverse domains within cyber-physical systems (CPS). CPS devices equipped with networked cameras are wirelessly connected to servers for complex vision-based analytics and intelligent operations. Adaptive video streaming is a pivotal technique in these applications to effectively deliver video content to servers under varying network conditions, enabling complex analytics afterward. Our thorough data analysis reveals that conventional offline video streaming control policies cannot effectively adapt to the high dynamics in networks and industrial video scenes. This results in suboptimal analytic performance and necessitates online adaptation for streaming control policy models. Yet, updating control policy models requires ground-truth analytics results which are unavailable directly on end devices due to their limited capacity. Furthermore, naively streaming original videos to the server for online adaptation is greatly challenged by scarce and dynamic networks, leading to decreased accuracy and increased transmission costs. In this paper, we present RIVA, a novel Online Learning-enabled adaptive streaming framework for Real-time Industrial Video Analytics. To facilitate communication-efficient online retraining, we design a hierarchical reinforcement learning approach in which the upper-level module intelligently determines the timing for online retraining, balancing Quality of Service (QoS) improvement and communication cost. Meanwhile, the lower-level module dynamically allocates bitrate to maximize QoS. Extensive experiments based on real-world industrial video and network datasets demonstrate that our proposed framework achieves a 22.6% mean accuracy increase, a 64.9% decrease in the mean failure rate of video uploading, and a 60.2% mean latency decrease compared to the state-of-the-art solutions.