Optimized Multiuser Panoramic Video Transmission in VR: A Machine Learning-Driven Approach

In this paper, we propose a machine learning-driven model to optimize panoramic video transmission for multiple users in virtual reality environments. The model predicts users' future field of view (FOV) using historical head orientation data and video saliency information, enabling targeted video delivery based on individual perspectives. By segmenting panoramic videos into tiles and applying a pyramid coding scheme, we adaptively transmit high-quality content within users' FOVs while utilizing lower-quality transmissions for peripheral regions. This approach effectively reduces bandwidth consumption while maintaining a high-quality viewing experience. Our experimental results demonstrate that combining user viewpoint data with video saliency features significantly improves long-term FOV prediction accuracy, leading to a more efficient and user-centric transmission model. The proposed method holds great potential for enhancing the immersive experience of panoramic video streaming in VR, particularly in bandwidth-constrained environments.