2013 Poster Sessions : Confused, Timid, and Unstable: Picking a Video Streaming Rate is Hard

Student Name : Te-Yuan Huang
Advisor : Nick McKeown
Research Areas: Computer Systems
Abstract:
Today’s commercial video streaming services use dynamic rate selection to provide a high-quality user experience. Most services host content on standard HTTP servers in CDNs, so rate selection must occur at the client. We measure three popular video streaming services – Hulu, Netflix, and Vudu – and find that accurate client-side bandwidth estimation above the HTTP layer is hard. As a result, rate selection based on inaccurate estimates can trigger a feedback loop, leading to undesirably variable and low-quality video. We call this phenomenon the downward spiral effect, and we measure it on all three services, present insights into its root causes, and validate initial solutions to prevent it.

Bio:
Te-Yuan (TY) is currently a Ph.D. candidate in Computer Science department in Stanford University, working with Prof. Nick McKoewn and Prof. Ramesh Johari. She is generally interested in multimedia networking and client-side network stack design. Before joining Stanford, she received her M.S. from National Taiwan University in 2008 and her B.S. from National Chiao-Tung University. Te-Yuan is a recipient of Stanford Graduate Fellowship in 2008, Google Fellowship in 2012 and IRTF Applied Networking Research Prize in 2013.