Generalizing Rate Control Strategies for Realtime Video Streaming via Learning from Deep Learning

The leading learning-based rate control method, i.e., QARC, achieves state-of-the-art performances but fails to interpret the fundamental principles, and thus lacks the abilities to further improve itself efficiently. In this paper, we propose EQARC (Explainable QARC) via reconstructing QARC's modules, aiming to demystify how QARC works. In details, we first utilize a novel hybrid attention-based CNN+GRU model to re-characterize the original quality prediction network and reasonably replace the QARC's 1D-CNN layers with 2D-CNN layers. Using trace-driven experiment, we demonstrate the superiority of EQARC over existing state-of-the-art approaches. Next, we collect several useful information from each interpretable modules and learn the insight of EQARC. Following this step, we further propose AQARC (Advanced QARC), which is the light-weighted version of QARC. Experimental results show that AQARC achieves the same performances as the QARC with an overhead reduction of 90%. In short, through learning from deep learning, we generalize a rate control method which can both reach high performance and reduce computation cost.