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Matthew Trunnell

dblp:140/7528 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Content delivery and video streaming · 62% Network measurement and analytics · 38%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Content delivery and video streaming
adaptive video streaming
0.212014
A buffer-based approach to rate adaptation: evidence from a large video streaming service · SIGCOMM 2014
Network measurement and analytics › bandwidth estimation
capacity estimation
0.112014
A buffer-based approach to rate adaptation: evidence from a large video streaming service · SIGCOMM 2014
Network measurement and analytics › web measurement
user behavior measurement
0.112014
A buffer-based approach to rate adaptation: evidence from a large video streaming service · SIGCOMM 2014
YearPublicationVenuePosition
2014 A buffer-based approach to rate adaptation: evidence from a large video streaming service
abstract
Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we suggest an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask when capacity estimation is needed. We test the viability of this approach through a series of experiments spanning millions of real users in a commercial service. We start with a simple design which directly chooses the video rate based on the current buffer occupancy. Our own investigation reveals that capacity estimation is unnecessary in steady state; however using simple capacity estimation (based on immediate past throughput) is important during the startup phase, when the buffer itself is growing from empty. This approach allows us to reduce the rebuffer rate by 10-20% compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate, and a higher video rate in steady state.
Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, Mark Watson
SIGCOMM4