Cutting the Cord in Virtual Reality

Today's virtual reality (VR) headsets require a cable connection to a PC or game console. This cable significantly limits the player’s mobility and hence her/his VR experience. The high data rate requirement of this link (multiple Gbps) precludes its replacement by WiFi. Thus, in this paper, we focus on using mmWave technology to deliver multi Gbps wireless communication between VR headsets and their game consoles. The challenge, however, is that mmWave signals can be easily blocked by the player's hand or head motion. We describe novel algorithms and system design that allow such mmWave links to sustain high data rates even in the presence of a blockage, enabling a high quality untethered VR experience.National Science Foundation (U.S.)Hong Kong University of Science and Technolog

Reliable Video Streaming over mmWave with Multi Connectivity and Network Coding

The next generation of multimedia applications will require the telecommunication networks to support a higher bitrate than today, in order to deliver virtual reality and ultra-high quality video content to the users. Most of the video content will be accessed from mobile devices, prompting the provision of very high data rates by next generation (5G) cellular networks. A possible enabler in this regard is communication at mmWave frequencies, given the vast amount of available spectrum that can be allocated to mobile users; however, the harsh propagation environment at such high frequencies makes it hard to provide a reliable service. This paper presents a reliable video streaming architecture for mmWave networks, based on multi connectivity and network coding, and evaluates its performance using a novel combination of the ns-3 mmWave module, real video traces and the network coding library Kodo. The results show that it is indeed possible to reliably stream video over cellular mmWave links, while the combination of multi connectivity and network coding can support high video quality with low latency.Comment: To be presented at the 2018 IEEE International Conference on Computing, Networking and Communications (ICNC), March 2018, Maui, Hawaii, USA (invited paper). 6 pages, 4 figure

Poster: Enabling Flexible Edge-assisted XR

Extended reality (XR) is touted as the next frontier of the digital future. XR includes all immersive technologies of augmented reality (AR), virtual reality (VR), and mixed reality (MR). XR applications obtain the real-world context of the user from an underlying system, and provide rich, immersive, and interactive virtual experiences based on the user's context in real-time. XR systems process streams of data from device sensors, and provide functionalities including perceptions and graphics required by the applications. These processing steps are computationally intensive, and the challenge is that they must be performed within the strict latency requirements of XR. This poses limitations on the possible XR experiences that can be supported on mobile devices with limited computing resources. In this XR context, edge computing is an effective approach to address this problem for mobile users. The edge is located closer to the end users and enables processing and storing data near them. In addition, the development of high bandwidth and low latency network technologies such as 5G facilitates the application of edge computing for latency-critical use cases [4, 11]. This work presents an XR system for enabling flexible edge-assisted XR.Comment: extended abstract of 2 pages, 1 figure, 2 table

FoVR: Attention-based VR Streaming through Bandwidth-limited Wireless Networks

Consumer Virtual Reality (VR) has been widely used in various application areas, such as entertainment and medicine. In spite of the superb immersion experience, to enable high-quality VR on untethered mobile devices remains an extremely challenging task. The high bandwidth demands of VR streaming generally overburden a conventional wireless connection, which affects the user experience and in turn limits the usability of VR in practice. In this paper, we propose FoVR, attention-based hierarchical VR streaming through bandwidth-limited wireless networks. The design of FoVR stems from the insight that human's vision is hierarchical, so that different areas in the field of view (FoV) can be served with VR content of different qualities. By exploiting the gaze tracking capacity of the VR devices, FoVR is able to accurately predict the user's attention so that the streaming of hierarchical VR can be appropriately scheduled. In this way, FoVR significantly reduces the bandwidth cost and computing cost while keeping high quality of user experience. We implement FoVR on a commercial VR device and evaluate its performance in various scenarios. The experiment results show that FoVR reduces the bandwidth cost by 88.9% and 76.2%, respectively compared to the original VR streaming and the state-of-the-art approach

Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.Comment: submitted to IROS 201