2014 Poster Sessions : A Buffer-Based Approach to Video Rate Adaptation

Student Name : Te-Yuan Huang
Advisor : Ramesh Johari
Research Areas: Computer Systems
During peak viewing time, well over 50% of US Internet traffic is
streamed video from Netflix and YouTube. To provide a better streaming
experience, these services select their video rates by observing and
estimating the available capacity. However, accurate estimation is
difficult due to highly variable throughput and complex interactions
between layers. As a result, existing algorithms often lead to
suboptimal video quality and unnecessary rebuffers.

This work proposes an alternative buffer-based approach. Rather
than presume that capacity estimation is always required, this
approach only uses the playback buffer and avoid capacity estimation,
unless the buffer itself is still growing from empty right after the
video starts. This approach is tested with a series of field
experiments spanning millions of Netflix users from May to September,
2013. The results reveal that although capacity estimation is
important during the startup phase, capacity estimation is unnecessary
in steady state. This simpler buffer-based approach allows us to
reduce the rebuffer rate by 10-20% compared to a production algorithm,
while delivering a similar overall average video rate, and a higher
video rate in steady state.

Te-Yuan 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 client-side network stack design and multimedia networking. Before joining Stanford, she received her M.S. from National Taiwan University in 2008 and her B.S. from National Chiao-Tung University in 2006. Te-Yuan is a recipient of Stanford Graduate Fellowship (2008-2012), Google Fellowship (2012-2014), and IETF/IRTF Applied Networking Research Prize, 2013.