Confidence-Aware 6DoF Mixed Reality Streaming under Error-Prone FoV Prediction

The application of mixed reality (MR) is gaining popularity as it allows virtual objects to be overlaid onto the real world, contributing to the development of the metaverse. This research paper introduces an innovative MR streaming system that takes into account confidence levels, with the goal of enhancing the quality of experience (QoE) and optimizing resource allocation in the presence of network limitations. Alongside facilitating user movements in six degrees of freedom, a problem of classifying the field of view (Fo V) is devised to predict Fo V centers and confidence scores. This approach is used to construct a novel visibility likelihood map. To the best of our knowledge, this is the first study to address the challenge of error-prone Fo V prediction to such an extent that no basic resources are required for all tiles. The results demonstrate a utility increase of approximately 34 % compared to alternative methods. This increase is observed when the system reaches its maximum capacity at a bandwidth constraint of 20 Mb/s. with minimal missing tiles.