Low-latency Cloud-based Volumetric Video Streaming Using Head Motion Prediction
This addresses latency issues for users of immersive volumetric video on mobile devices, but it is incremental as it builds on existing streaming systems.
The paper tackles the problem of high motion-to-photon latency in cloud-based volumetric video streaming by developing a head motion prediction model, which reduces rendering errors compared to a baseline without prediction.
Volumetric video is an emerging key technology for immersive representation of 3D spaces and objects. Rendering volumetric video requires lots of computational power which is challenging especially for mobile devices. To mitigate this, we developed a streaming system that renders a 2D view from the volumetric 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 to compensate the added latency, prediction of the future user pose is necessary. We developed a head motion prediction model and investigated its potential to reduce the M2P latency for different look-ahead times. Our results show that the presented model reduces the rendering errors caused by the M2P latency compared to a baseline system in which no prediction is performed.