A Deep Reinforcement Learning Approach to Balance Viewport Prediction and Video Transmission in 360° Video Streaming

360° video streaming has seen tremendous growth in past years. However, our measurement reveals a dilemma that severely limits QoE. On the one hand, viewport prediction requires the shortest possible prediction distance for high predicting accuracy; On the other hand, video transmission requires more buffered data to compensate for bandwidth fluctuations otherwise substantial playback rebuffering would be incurred. Since no existing method can break this dilemma, the QoE optimization was naturally bottlenecked. This work tackles this challenge by developing QUTA – a novel learning-based streaming system. Specifically, our measurement shows that three kinds of internal streaming parameters have significant impacts on the prediction distance, namely, download pause, data rate threshold, and playback rate. On top of this, we design a new long-term-planning (LTP) learning method that tunes the parameters dynamically based on the network and streaming context. Evaluations with large-scale streaming trace data show that QUTA not only improves the prediction accuracy and QoE by up to 68.4% but also exhibits strong robustness.