Adaptive 360-Degree Streaming: Optimizing With Multi-Window and Stochastic Viewport Prediction

The tile-based approach is widely adopted in adaptive 360-degree video streaming systems, due to its efficiency in managing limited bandwidth resources. Recently, significant research efforts have been devoted to viewport-prediction-enabled bitrate adaptation for tile-based 360-degree Adaptive Bit-Rate (ABR) streaming, towards improving the average video quality while reducing rebuffering. However, the inherent uncertainty of users’ viewports has posed limitations on users’ Quality of Experience (QoE) for tile-based 360-degree ABR streaming. In this paper, we introduce a multi-window and stochastic viewport prediction approach to address the viewport uncertainty. In particular, considering our goal of maximizing the expectation of future QoE, we investigate a viewport distribution prediction model, to cope with the inherent randomness. Additionally, to accommodate the varying gap between the playback and the download process, we explore the multiple-window viewport prediction models to capture different prediction gaps. Even with the utilization of distributional prediction and multi-window models, predicting viewports far into the future is still inherently challenging. Accordingly, we propose a patience pattern temporarily suspending the download process, allowing for the accumulation of additional head movement trajectory data. Finally, we employ a model predictive control (MPC) approach for sequential decision-making, formulating the MPC problem as a mixed-integer non-linear programming (MINLP) task. To mitigate the computational burden associated with solving MINLP, we introduce a mixed-integer linear programming transformation to achieve efficient decision-making. Extensive experiments, utilizing real-world traces and user head movement trajectories, demonstrate that the proposed method outperforms state-of-the-art methods, improving overall QoE performance by 16.75% –18.91% .