Low-latency Cloud-based Volumetric Video Streaming Using Head Motion Prediction

Volumetric video is an emerging key technology for immersive representationof 3D spaces and objects. Rendering volumetric video requires lots ofcomputational power which is challenging especially for mobile devices. Tomitigate this, we developed a streaming system that renders a 2D view from thevolumetric video at a cloud server and streams a 2D video stream to the client.However, such network-based processing increases the motion-to-photon (M2P)latency due to the additional network and processing delays. In order tocompensate the added latency, prediction of the future user pose is necessary.We developed a head motion prediction model and investigated its potential toreduce the M2P latency for different look-ahead times. Our results show thatthe presented model reduces the rendering errors caused by the M2P latencycompared to a baseline system in which no prediction is performed.