When someone streams a movie, TV show, or video call on today’s internet, it’s usually still a one-on-one conversation between device and content provider, built from scratch. With each use, an application tries to best utilize its existing network conditions, which can lead to loss of quality, buffering, or full drops when those parameters change. Providers can try to bolster their side of the exchange by scaling up their resources, but still largely deliver their data reactively in response to each user’s individual environment.
To find a new protocol for delivering better streaming quality, new UChicago CS assistant professor Junchen Jiang blended two distinct areas of computer science: networked systems and machine learning. Through work with companies such as Microsoft, Google, and Conviva, Jiang observed that content providers monitor performance metrics for the use of their internet applications, but rarely turn around that data to improve quality. In his PhD thesis at Carnegie Mellon University, he proposed a top-down, data-driven approach to optimization that can improve user’s quality of experience in streaming applications.
“What if we have visibility across millions of users, and when these users connect to the same network, they're actually probing what's happening in the network and provide a comprehensive view of the whole internet?” Jiang said. “Based on that information, you can map the internet and try to monitor where is the problem space, what are the hotspots, where are the performance bottlenecks. From there, it opens up a whole new area: Using a data-driven approach to solve networking problems.”