CAREER: Enabling Perception-Driven Optimization for Online Videos

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Today, video streaming is no longer just about entertainment but essential to our daily life. For example, high-resolution live videos are used for daily (remote) learning, and videos streamed from road-side sensors are constantly analyzed to make our cities and roads safer. These changes lead to a higher need for network bandwidth to reach desired Quality of Experience (QoE).

This is challenging, because to serve all these demands, today’s video delivery systems must either lower video quality or upgrade the network infrastructure (which can be slow and expensive). This project develops Perception-Driven Optimization (PDO), a new paradigm that improves user experience for today’s online videos without using more bandwidth.

The key insight is that in terms of how video quality impacts QoE, significant heterogeneities exist across videos, human users, and video analytic models, which are hard to capture by today’s offline QoE models. PDO takes a data-driven approach to automate online QoE modeling, by leveraging the large number of video sessions available to today’s video delivery systems.

This research entails three thrusts: (a) for live video services, how to automate QoE modeling in live content with a minimal number of online sessions; (b) for on-demand videos, how to build per-user QoE models without impacting user experience; and (c) for video-analytics services, how to profile video quality’s impact on video-analytics mode. PDO also integrates online QoE modeling with today’s video-delivery systems to better adapt to changes in network conditions.

This project works with industry partners in online video services and edge video analytics, and local initiatives to deploy PDO and improve online video QoE, especially for users who suffer quality issues, and scale edge analytics to more sensor videos. It also creates new ways to tie system/networking education with everyday use of the Internet, such as new educational tools that visualize how network performance affects user-perceived video quality and video-based intelligent applications.

The project also engages with students through Leadership Alliance which targets underrepresented populations, and the developed educational tools will be used for compileHer, a program that targets high school female students.

The software and research artifacts implemented as part of this project are released on a public website: https://people.cs.uchicago.edu/~junchenj/perception_driven_optimization. The site is regularly maintained and includes released data, source code, and reproduction instructions.

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.