Pandia: Open-source Framework for DRL-based Real-time Video Streaming Control

Deep Reinforcement Learning (DRL) has rapidly gained traction as a viable method for optimizing control in real-time video streaming. Recent research endeavors are shifting towards enabling direct DRL control over multiple streaming parameters, moving away from traditional bitrate only control. Despite this growing interest, there is a notable absence of a dedicated open-source framework to facilitate such research. In response, we introduce Pandia, a specialized open-source framework designed for the direct manipulation of multiple realtime video streaming parameters using DRL. Pandia effectively bridges advanced DRL frameworks like OnRL and SB3 with the WebRTC, a leading real-time video streaming platform. Our initial use case with Pandia showcases its capability in advancing DRL application in WebRTC control. Our study identifies significant training challenges in basic network settings, mainly due to negative impacts from random exploration. To counter this, we adopt a curriculum learning approach, using domain knowledge for more effective, guided exploration. Both the training methodology outlined in our use case and the Pandia framework are poised to contribute to ongoing research in DRL's application to WebRTC control.