SBIR Phase I: An AI-Based Collaborative Video Streaming Platform

Award ID: 2112229

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve video streaming for applications ranging from consumer viewing to remote education. The proposed platform reduces the need for data centers and Content Distribution Networks (CDNs), leading to cost reductions for video delivery. By potentially reducing network congestion and increasing network resilience to failures, the resulting solution can help improve viewers experience. Moreover, because video streaming represents 80+% of internet traffic, the proposed technology will contribute to a reduction in the energy consumption and carbon footprint of the cloud computing infrastructures currently used for this service. This Small Business Innovation Research (SBIR) Phase I project leverages and extend state-of-the-art research in artificial intelligence, stochastic systems and edge networks, addressing three key challenges: (1) Design of a decentralized artificial intelligence optimization algorithm that efficiently manages video traffic; (2) Design of an advanced simulation environment capable of efficiently mimicking the behavior of real-world networks of users for system design and assessment; (3) System optimization for use on a wide range of devices. 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.

NSF Program Director: Peter Atherton