CAREER: Wireless Collaborative Mixed Reality Networking: Foundations and Algorithms for Joint Communication, Computation, and Learning

职业：无线协作混合现实网络：联合通信、计算和学习的基础和算法

• 批准号: 2152658
• 负责人: Bin Li
• 金额: $ 50.59万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152658&HistoricalAwards=false
• 关键词: CAREER; Wireless; Collaborative; Mixed; Reality

Wireless collaborative mixed reality (WCMR) provides an interactive and immersive experience for a group of people that can move freely in an open space and will potentially revolutionize existing collaborative mission-critical training, such as firefighter drills and disaster response training. In order to provide the best immersive experience, WCMR differs drastically from traditional wireless applications in that they demand not only coordinated information to be transferred to collaborating agents in real-time but also fast computations in mobile mixed reality devices. Hence, WCMR requires fundamentally different designs than existing approaches that mainly focus on communication demands and predominantly assume that they are independently generated at different network agents. This project aims to develop joint communication, computation, and learning algorithms that explicitly exploit unique characteristics of WCMR and support emerging WCMR applications. Research outcomes from this CAREER project are constantly integrated into both undergraduate and graduate courses taught by the PI. This CAREER project also establishes outreach programs for both K-12 and college students to be exposed to state-of-the-art wireless and mixed reality technologies. Different from traditional wireless networks, the design of efficient WCMR needs to enable both concurrent wireless communications and fast computations. Therefore, the system performance relies heavily on the extremely low-delay completion of all concurrent communication and computation tasks across the network, instead of independent communication tasks as in traditional wireless networks. As such, the proposed research is organized into the following three interdependent thrusts: (i) Serving concurrent WCMR traffic. This thrust focuses on the communication aspect of WCMR and will establish analytical foundations of adaptive algorithm design that efficiently serves the concurrent traffic with the goal of optimizing throughput, latency, and seamless user experience. (ii) Offloading compute-intensive WCMR tasks. This thrust addresses both communication and computation needs of WCMR, and will develop joint offloading and scheduling schemes that significantly boost the performance of WCMR to alleviate heavy computations in mobile mixed reality devices by leveraging powerful servers. (iii) Leveraging predictable WCMR user behavior. This thrust focuses on joint communication, computation, and learning design, and will further enhance network performance by exploiting predictable user behavior. Finally, we will implement the algorithms developed in this project in our existing platforms, and evaluate their corresponding performance.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.

无线协作混合现实 (WCMR) 为一群人提供了交互式和沉浸式体验，可以在开放空间中自由移动，并有可能彻底改变现有的协作关键任务培训，例如消防员演习和灾难响应培训。为了提供最佳的沉浸式体验，WCMR 与传统无线应用程序有很大不同，因为它们不仅需要将协调信息实时传输到协作代理，还需要移动混合现实设备中的快速计算。因此，WCMR 需要与主要关注通信需求并主要假设它们是在不同网络代理处独立生成的现有方法完全不同的设计。该项目旨在开发联合通信、计算和学习算法，明确利用 WCMR 的独特特征并支持新兴的 WCMR 应用。该职业项目的研究成果不断融入 PI 教授的本科生和研究生课程中。该职业项目还为 K-12 学生和大学生制定了外展计划，让他们接触最先进的无线和混合现实技术。与传统无线网络不同，高效WCMR的设计需要同时实现并发无线通信和快速计算。因此，系统性能在很大程度上依赖于网络上所有并发通信和计算任务的极低延迟完成，而不是像传统无线网络中那样独立的通信任务。因此，拟议的研究分为以下三个相互依赖的主旨：(i) 服务并发 WCMR 流量。该主旨重点关注 WCMR 的通信方面，并将建立自适应算法设计的分析基础，有效地服务于并发流量，以优化吞吐量、延迟和无缝用户体验。 (ii) 卸载计算密集型 WCMR 任务。这一推动力解决了 WCMR 的通信和计算需求，并将开发联合卸载和调度方案，显着提高 WCMR 的性能，通过利用强大的服务器来减轻移动混合现实设备中的繁重计算。 (iii) 利用可预测的 WCMR 用户行为。该主旨侧重于联合通信、计算和学习设计，并将通过利用可预测的用户行为进一步增强网络性能。最后，我们将在现有平台上实施该项目开发的算法，并评估其相应的性能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

Enhancing Quality of Experience for Collaborative Virtual Reality with Commodity Mobile Devices

• DOI: 10.1109/icdcs54860.2022.00102
• 发表时间: 2022-07
• 期刊: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Jiangong Chen;Feng Qian;Bin Li

Efficient Distributed Threshold-Based Offloading for Large-Scale Mobile Cloud Computing

• DOI: 10.1109/tnet.2022.3193073
• 发表时间: 2023-02
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Xudong Qin;Bin Li;Lei Ying

A Scalable Mixed Reality Platform for Remote Collaborative LEGO Design

用于远程协作乐高设计的可扩展混合现实平台

• DOI: 10.1109/infocomwkshps54753.2022.9798010
• 发表时间: 2022
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS
• 作者: Yao, Xinyi;Chen, Jiangong;He, Ting;Yang, Jing;Li, Bin
• 通讯作者: Li, Bin

Online Learning-Based Rate Selection for Wireless Interactive Panoramic Scene Delivery

• DOI: 10.1109/infocom48880.2022.9796965
• 发表时间: 2022-05
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Harsh Gupta;Jiangong Chen;Bin Li;R. Srikant