JSFBA: Joint Segment and Frame Bitrate Adaptation for Real-Time Video Analytics

Due to the intensive computing resource requirements, real-time video analytics applications typically need to transmit video to a server. However, the transmission inevitably suffers from network bandwidth limitations and fluctuations, making it challenging to guarantee video analytics performance. In this paper, we aim to maximize video analytics accuracy while maintaining low latency and frame loss rate, and propose a joint segment and frame bitrate adaptation (JSFBA) framework for real-time video analytics, which incorporates two reinforcement learning-based algorithms to adapt to bandwidth at both the segment and frame levels. Initially, considering the effect of video encoding on video analytics, we employ a bitrate control method to design a segment-level bitrate adaptation (SLBA) algorithm with a unique reward function. Based on the historical information of the video segments, SLBA selects the appropriate bitrate for each segment. Subsequently, by leveraging the ability to generate multiple bitrates in scalable video coding (SVC), we design a frame-level bitrate adaptation (FLBA) algorithm, which adapts to bandwidth in a more fine-grained manner by determining the number of layers sent for each frame. Extensive experiments on large-scale network traces reveal that JSFBA effectively balances various video analytics performance metrics and achieves maximum utility compared to state-of-the-art solutions.