A DRL-Based Real-Time Video Processing Framework in Cloud-Edge Systems

Nowadays, the Internet is rapidly evolving toward the future of the Internet of Things (IoT), where billions or even trillions of edge devices may be interconnected. The proliferation of network cameras and the advancement of IoT technologies have provided broader opportunities for data collection and utilization. In the past, the massive real-time videos generated by network cameras were mostly transmitted over the network to the cloud for analysis. However, due to network speed limitations, the latency incurred by uploading all videos to the cloud makes it difficult to meet the real-time requirements of video analysis. While edge computing significantly reduces latency, the computational capabilities of edge devices are limited, making it difficult to handle large amounts of real-time video data. In this article, we introduce a real-time video processing framework called DeepVA, which utilizes cloud-edge collaboration technology to reduce latency in real-time video processing and enhance the accuracy of analysis. The DeepVA framework incorporates the DRLVA video frame distribution algorithm based on deep reinforcement learning (DRL), which dynamically determines whether to distribute video frames for processing at the cloud or edge. To evaluate the performance of the proposed DRLVA algorithm, we first verify that it is superior to several other DRL-based distribution algorithms on the Gym environment. We also evaluate the performance of DeepVA on the MOT2015 data set, MOTSynth data set, and real campus surveillance videos. The experiments show that our DeepVA outperforms both cloud-only and edge-only solutions in terms of reducing latency and improving accuracy.