Multi-scale Historical Trajectory Decomposition for Viewport Prediction in 360-degree Videos

360-degree panoramic video provides users with an unprecedented immersive experience and is rapidly evolving with the support of virtualized devices. Effective viewport prediction is crucial for alleviating high-speed bandwidth constraints and enhancing user quality of service. However, most existing research relies on saliency maps derived from multi-user viewport trajectories, often neglecting the rich multi-scale information inherent in individual user viewport trajectory. Inspired by signal decomposition theory, we propose an Empirical Mode Decomposition-based LSTM-MLP (EMD-ML) model. The EMD-ML extracts robust spatiotemporal representations from user viewport trajectory at multiple scales. Leveraging multi-scale representations and a hybrid learning framework, the model achieves accurate long-term viewport prediction from limited short-term viewport trajectory. The EMD-ML model achieves nearly a 50% reduction in orthodromic distance compared to state-of-the-art methods across four publicly available datasets. Additionally, experiments on different video types and training datasets show that our method generalizes well and can be applied in real-world scenarios.