  While video streaming algorithms are a hot research area, with interesting
new approaches proposed every few months, little is known about the behavior of
the streaming algorithms deployed across large online streaming platforms that
account for a substantial fraction of Internet traffic. We thus study adaptive
bitrate streaming algorithms in use at 10 such video platforms with diverse
target audiences. We collect traces of each video player's response to
controlled variations in network bandwidth, and examine the algorithmic
behavior: how risk averse is an algorithm in terms of target buffer; how long
does it takes to reach a stable state after startup; how reactive is it in
attempting to match bandwidth versus operating stably; how efficiently does it
use the available network bandwidth; etc. We find that deployed algorithms
exhibit a wide spectrum of behaviors across these axes, indicating the lack of
a consensus one-size-fits-all solution. We also find evidence that most
deployed algorithms are tuned towards stable behavior rather than fast
adaptation to bandwidth variations, some are tuned towards a visual perception
metric rather than a bitrate-based metric, and many leave a surprisingly large
amount of the available bandwidth unused.
