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Enabling Wireless VR using Predictive Intelligence and Edge-Computing


Triggered by several head-mounted display (HMD) devices that have come to the market in recent years, such as Oculus Rift, HTC Vive, and Samsung Gear VR, significant interest has developed in virtual reality (VR) systems, experiences and applications. However, the current HMD devices are either tethered with PC/console, or rendering locally on itself (quite clunky to wear), negatively affecting user experience.

This thesis presents innovative methodologies to enable a truly portable and mobile VR experience, with lightweight VR glasses wirelessly connecting with edge/cloud computing devices that perform the rendering. There are two main challenges for this edge/cloud-based solution: (i) ultra-high bandwidth needed to transmit encoded video, and (ii) ultra-low latency needed to avoid user's dizziness feeling.

To address the challenging requirements of bandwidth and latency, this thesis presents three methodologies. Firstly, we have investigated and developed a novel hybrid-cast approach to save bandwidth in a multi-user streaming scenario. We identify and broadcast the common pixels shared by multiple users, while unicast the residual pixels for each user. We formulate the problem of minimizing the total bitrate needed to transmit the user views using hybrid-casting and present a common view extraction approach and a smart grouping algorithm to achieve our hybrid-cast approach. Secondly, we have explored 360-degree video and three Degrees of Freedom (3DoF) VR streaming scenario, and proposed a predictive adaptive streaming approach to reduce latency needed by pre-delivery. By streaming the predicted view with high predictive probability in relatively high quality according to bandwidth conditions and transmitted in advance, we aim to address both latency and constrained wireless bandwidth challenges. Thirdly, for six Degrees of Freedom (6DoF) VR content, we have developed predictive pre-rendering approach to reduce latency needed. By predicting both head and body motions, our approach can pre-render the predicted view in advance and thus save latency in 6DoF VR scenarios.

This thesis concludes with a summary of its contributions and open directions for future research.

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