Collaborative Research: CNS Core: Medium: Rethinking Multi-User VR - Jointly Optimized Representation, Caching and Transport

基于无线连接和资源受限设备的交互式虚拟现实（VR）在教育和医疗保健领域具有重要应用。由于高数据量和严格的延迟要求，支持这样的VR应用是具有挑战性的。该项目采用了一种新颖的整体方法来实现健壮和可管理的多用户VR应用程序支持。该项目共同开发数据表示、算法、协议和原型系统，以提高协作无线VR应用中的用户体验质量，同时降低对网络带宽、设备存储和计算的要求。该方法的核心是利用用户或用户组附近的“边缘云”资源。边缘云与用户设备和更远程的云协同工作，以优化用户体验，具有对网络可变性和临时中断的鲁棒性。边缘、远程云和用户设备之间的处理任务经过编排，以最好地利用用户和边缘之间的较低延迟。该项目以更少的硬件和网络需求为更多用户提供高质量的VR，使教育和医疗保健等领域的交互式可视化成为可能，这在以前是不可能的。拟议的研究将有助于以低成本提高高容量、延迟关键工作负载的用户体验质量。这项工作将为无线连接的瘦客户机、近边缘云和远程云之间的数据表示、缓存和传输提供策略。该项目将传播文件完备的软件和教育材料。这些软件原型将用于在各级计算机科学和工程领域招募代表性不足的少数族裔，并支持联合研究项目和本科生暑期实习。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Interactive Virtual Reality (VR) on wirelessly-connected and resource-constrained devices has important applications in education and healthcare. Supporting such VR applications is challenging due to the high data volume and stringent latency requirements. This project takes a novel holistic approach towards robust and manageable multi-user VR application support. The project jointly develops data representations, algorithms, protocols, and a prototype system that enhance the user quality of experience in collaborative, wireless VR applications, while reducing the requirements for network bandwidth, and for device storage and computation. Central to the approach is exploiting "edge cloud" resources that are near a user or group of users. The edge cloud works in concert with user devices and the more remote cloud to optimize user experience, with robustness to network variability and temporary outages. The processing tasks among the edge, remote cloud, and user devices are orchestrated to best exploit the lower latency between the users and the edge.The project makes high-quality VR accessible to more users, with fewer hardware and network requirements, enabling interactive visualization in domains such as education and healthcare at scales not previously possible. The proposed research will contribute to the general area of increasing user quality of experience at a low cost for high-volume, latency-critical workloads. The work will contribute strategies for data representation, caching, and transport among wirelessly-connected thin clients, a nearby edge cloud, and a remote cloud. The project will disseminate well-documented software and educational material. The software prototypes will be used to recruit under-represented minorities in computer science and engineering at all levels, and to support joint research projects and summer internships for undergraduates.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.