Accurate Bandwidth Prediction for Real-Time Media Streaming with Offline Reinforcement Learning

In real-time communication (RTC) systems, accurate bandwidth prediction is crucial for encoding and transmission strategies to optimize users' quality of experience (QoE) in various network environments. In this paper, we propose an offline reinforcement learning (RL) method to predict bandwidth for RTC video streaming. We use a representative algorithm, named Implicit Q-Learning (IQL), to train the model. To improve the performance, we carefully preprocess the given dataset and redesign the neural network structure and the reward function. Ablation studies are performed to verify our design choices. Furthermore, compared to a baseline method and six behavior policies, our method reduces the mean squared error (MSE) by 18%-22%, demonstrating high prediction accuracy. Our proposed method won the first prize in ACM MMSys 2024 Grand Challenge on Offline Reinforcement Learning for Bandwidth Estimation in Real Time Communications. The source code is available at https://github.com/n13eho/Schaferct.