Retina-U: A Two-Level Real-Time Analytics Framework for UHD Live Video Streaming

Abstract

UHD live video streaming, with its high video resolution, offers a wealth of fine-grained scene details, presenting opportunities for intricate video analytics. However, current real-time video streaming analytics solutions are inadequate in analyzing these detailed features, often leading to low accuracy in the analysis of small objects with fine details. Furthermore, due to the high bitrate and precision of UHD streaming, existing real-time inference frameworks typically suffer from low analyzed frame rate caused by the significant computational cost involved. To meet the accuracy requirement and improve the analyzed frame rate, we introduce Retina-U, a real-time analytics framework for UHD video streaming. Specifically, we first present SECT, a real-time DNN model level inference model to enhance inference accuracy in dynamic UHD streaming with an abundance of small objects. SECT uses a slicing-based enhanced inference (SEI) method and Cascade Sparse Queries (CSQ) based-fine tuning to improve the accuracy, and leverages a lightweight tracker to achieve high analyzed frame rate. At the system level, to further improve the inference accuracy and bolster the analyzed frame rate, we propose a deep reinforcement learning-based resource management algorithm for real-time joint network adaptation, resource allocation, and server selection. By simultaneously considering the network and computational resources, we can maximize the comprehensive analytic performance in a dynamic and complex environment. Experimental results demonstrate the effectiveness of Retina-U, showcasing improvements in accuracy of up to 38.01% and inference speed acceleration of up to 24.33%.