For 3DoF and 6DoF VR applications, the ultra-low latency requirement with user's head rotation and body movements can be very challenging to meet, as new video needs to be rendered in response to user's head and body motions, and then displayed on the HMD. Moreover, if a truly wireless experience is desired, the video needs to be rendered and streamed wirelessly from a local/edge computing device further adding to the delay. In this project, we focus on techniques to ensure the ultra-low latency requirement of head motion as well as body motion.

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As 360-degree videos and VR applications become popular for consumer and enterprise use cases, the desire to enable truly mobile experiences also increases. Delivering 360-degree videos and cloud/edge-based VR applications require ultra-high bandwidth and ultra-low latency, challenging to achieve with mobile networks. A common approach to reduce bandwidth is streaming only the field of view (FOV). However, extracting and transmitting the FOV in response to user head motion can add high latency, adversely affecting user experience. In this project, we propose a predictive view generation approach, where only the predicted view is extracted (for 360-degree video) or rendered (in case of VR) and transmitted in advance, leading to a simultaneous reduction in bandwidth and latency.

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We investigate virtual space applications such as virtual classroom and virtual gallery, in which the scenes and activities are rendered in the cloud, with multiple views captured and streamed to each end device. A key challenge is the high bandwidth requirement to stream all the user views, leading to high operational cost and potential large delay in a bandwidth-restricted wireless network. In a multi-user streaming scenario, we propose a hybrid-cast approach to save bandwidth needed to transmit the multiple user views, without compromising view quality.

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