Improving the Quality of Experience in Real-Time Communication Systems through Data-Driven Bandwidth Estimation with Deep Reinforcement Learning

Abstract

Real-Time Communication (RTC) systems have become increasingly popular, with accurate bandwidth estimation being a critical factor in ensuring Quality of Experience (QoE) for end users. Traditional probe-based and model-based methods for bandwidth estimation have limitations, such as introducing additional overhead or relying on assumptions that may not hold in dynamic network conditions. Datadriven approaches, particularly those using machine learning techniques, have shown promise but may require substantial amounts of labeled data and struggle to adapt to changing network conditions. In this thesis, we propose an offline deep reinforcement learning (DRL) approach for bandwidth estimation in RTC applications. Our method leverages historical network data to train an agent that learns an optimal bandwidth estimation policy without the need for explicit probing or labeled data. This approach expects to offer improved adaptability to dynamic network conditions, reduced overhead, and enhanced accuracy compared to traditional and data-driven methods. We evaluate the performance of our proposed method across various network scenarios. The results reveal valuable insights and highlight the potential of offline DRL for achieving reliable bandwidth estimation in RTC applications. To accommodate reproducibility, we have made our source code publicly available1.