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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF ILLINOIS
Doing Business As Name:University of Illinois at Urbana-Champaign
PD/PI:
  • Rayadurgam Srikant
  • (217) 333-2457
  • rsrikant@illinois.edu
Award Date:07/25/2021
Estimated Total Award Amount: $ 266,000
Funds Obligated to Date: $ 66,500
  • FY 2021=$66,500
Start Date:10/01/2021
End Date:09/30/2025
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: CNS Core: Medium: Foundations and Scalable Algorithms for Personalized and Collaborative Virtual Reality Over Wireless Networks
Federal Award ID Number:2106801
DUNS ID:041544081
Parent DUNS ID:041544081
Program:Networking Technology and Syst
Program Officer:
  • Alhussein Abouzeid
  • (703) 292-8950
  • aabouzei@nsf.gov

Awardee Location

Street:1901 South First Street
City:Champaign
State:IL
ZIP:61820-7406
County:Champaign
Country:US
Awardee Cong. District:13

Primary Place of Performance

Organization Name:University of Illinois at Urbana-Champaign
Street:506 S. Wright Street
City:Urbana
State:IL
ZIP:61801-3620
County:Urbana
Country:US
Cong. District:13

Abstract at Time of Award

Virtual reality (VR) over wireless networks can provide an interactive and immersive experience for multiple users simultaneously and thus has many applications, especially in VR-based education/training. However, satisfactory personalized user experience in such wireless immersive services demands stringent performance requirements, including: (1) high-speed and high-resolution panoramic image rendering; (2) extremely low delay guarantees; and (3) seamless user experience. Besides the aforementioned requirements, collaborative user experience requires both scalability and fairness of VR service. Existing VR systems heavily rely on various heuristic designs and do not efficiently exploit VR content commonality and its predictability, which impede their large-scale deployment. This project aims to develop the theoretical foundations and complete implementation of a system for providing both personalized and scalable collaborative VR experience over wireless networks. This project will integrate machine learning, wireless networking, and mobile computing to enable high-quality and scalable wireless immersive applications on commodity mobile devices. The theory and practical implementations to be developed in this project will be integrated into both undergraduate and graduate curriculum, as well as exposing K-12 students to state-of-the-art wireless and VR technologies. The proposed designs are motivated by a number of insights that we have developed from our preliminary work, including (1) viewport-adaptive rendering; (2) commonality among VR content for multiple users to enable multicasting; and (3) predictability of VR content to enable prefetching. The proposed research will contribute to and advance both theoretical and system-oriented research in the fields of wireless networks and virtual reality. The project explicitly exploits the unique characteristics of both immersive VR applications and wireless networks, and propose the following four interdependent research thrusts: (I) Dealing with network and prediction uncertainties: This thrust will investigate algorithm designs to optimize personalized user experience given both network and viewport prediction uncertainties. (II) Meeting stringent immersive service requirements: This thrust will develop wireless scheduling algorithms that provide stringent immersive, personalized service guarantees for multiple VR users. (III) Supporting smooth collaborative interaction: This thrust will focus on the algorithm design that leverages the VR content similarities and predictabilities that naturally emerge during collaborative interactions. (IV) Scalable system integration, implementation, evaluation, and deployment: This thrust will integrate Research Thrusts I through III into a holistic system, perform system-level optimizations, and evaluate it through lab experiments and real-world classroom deployment. 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.

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