A buffer-based approach to video rate adaptation

Placeholder Show Content


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 adapt their video rates by observing and estimating the available capacity. However, accurate capacity estimation is difficult due to highly variable throughput and complex interactions between layers. As a result, existing rate adaptation algorithms often lead to suboptimal video quality and unnecessary rebuffers. This thesis proposes an alternative buffer-based approach to adapt video rate. Rather than presuming that capacity estimation is always required, this approach starts the design by only using the playback buffer occupancy, and then ask when capacity estimation can be helpful. This design process leads to two separate phases of operation: during the steady-state phase, when the buffer encodes adequate information, we choose the video rate based only on the playback buffer; during the startup phase, when the buffer contains little information, we augment the buffer-based design with capacity estimation. This approach is tested with a series of field experiments spanning millions of Netflix users from May to September, 2013. The results demonstrate that although a simple capacity estimation is important during the startup phase, it is unnecessary in the steady state. The buffer-based approach allows us to reduce the rebuffer rate by 10-20% compared to a commercial algorithm used in Netflix, while delivering a similar overall average video rate and a higher video rate in steady state.


Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2014
Issuance monographic
Language English


Associated with Huang, Deyuan
Associated with Stanford University, Department of Computer Science.
Primary advisor McKeown, Nick
Thesis advisor McKeown, Nick
Thesis advisor Johari, Ramesh, 1976-
Thesis advisor Katti, Sachin
Advisor Johari, Ramesh, 1976-
Advisor Katti, Sachin


Genre Theses

Bibliographic information

Statement of responsibility Te-Yuan Huang.
Note Submitted to the Department of Computer Science.
Thesis Thesis (Ph.D.)--Stanford University, 2014.
Location electronic resource

Access conditions

© 2014 by Te-Yuan Huang
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

Also listed in

Loading usage metrics...