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


Principal Investigator

  • Bin Li, Assistant Professor, University of Rhode Island

Graduate Students

Project Goals

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.


My students are marked by *.

  1. Jiangong Chen*, Xudong Qin*, Guangyu Zhu, Bo Ji, Bin Li, Motion-Prediction-based Wireless Scheduling for Multi-User Panoramic Video Streaming, In Proc. IEEE International Conference on Computer Communications (INFOCOM), May, 2021. [ Acceptance rate: 19.9%]

  2. Bin Li, Efficient Learning-based Scheduling for Information Freshness in Wireless Networks, In Proc. IEEE International Conference on Computer Communications (INFOCOM), May, 2021. [ Acceptance rate: 19.9%]

  3. Xudong Qin*, Bin Li, Lei Ying, Distributed Threshold-based Offloading for Large-Scale Mobile Cloud Computing, In Proc. IEEE International Conference on Computer Communications (INFOCOM), May, 2021. [ Acceptance rate: 19.9%]

  4. Jiangong Chen*, Feng Qian, Bin Li, An Interactive and Immersive Remote Education Platform based on Commodity Devices, In Proc. IEEE International Conference on Computer Communications (INFOCOM), May, 2021 (demo paper).

  5. Bin Li, Jia Liu, Achieving Information Freshness with Selfish and Rational Users in Mobile Crowd-Learning, accepted by IEEE Selected Areas in Communication (JSAC), 2021.

  6. Fengjiao Li, Yu Sang, Zhongdong Liu, Huasen Wu, Bin Li, Bo Ji, Waiting but not Aging: Optimizing Information Freshness under the Pull Model, accepted by IEEE/ACM Transactions on Networking (ToN), 2020.

  7. Jiangong Chen*, Bin Li, R. Srikant, Thompson-Sampling-Based Wireless Transmission for Panoramic Video Streaming, In WiOpt Workshop on Machine Learning in Wireless Communications (WMLC), June, 2020. (invited paper)

Educational Activities

  • [September 2020]: Dr. Bin Li co-organized URI second Immerse-a-thon event for exploring virtual/augmented applications beyond gaming. He and his Ph.D. student, Jiangong Chen, delivered the talk on the virtual/augmented reality (VR/AR) technology and basic VR/AR application development, respectively.

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  • In Fall 2020, Dr. Bin Li taught ELE 437/CSC 417 Introduction to Computer Networks. He introduced the networking technology for supporting virtual/augmented reality applications.

  • In Fall 2020, Dr. Bin Li was invited to introduce how virtual/augmented reality can improve the learning efficiency of the remote education during COVID-19 for engineering freshmen in the course EGR 101x Engineering's Response to COVID-19.

  • In Fall 2020, Dr. Bin Li was invited to introduce virtual/augmented technology in the university grand challenge course ELE 108G Cyber-Physical Security.

Outreach Activities

  • [September 2020]: Dr. Bin Li was invited to be a guest in The Peggy Smedley Show to introduce mixed reality technology and its application in firefighter training. The Peggy Smedley Show is the No. 1 IoT and digital transformation podcast.

  • [September 2020]: Dr. Bin Li was invited to be a panelist for the panel on AR/VR/XR over wireless networks: challenges and opportunities in the 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WOWMOM 2020).