On Crowdsourced Interactive Live Streaming: A Twitch.TV-Based
Measurement Study
release_wkvfuoedwfe75e3p5otqkuvi7e
by
Cong Zhang, Jiangchuan Liu
2015
Abstract
Empowered by today's rich tools for media generation and collaborative
production, the multimedia service paradigm is shifting from the conventional
single source, to multi-source, to many sources, and now toward
crowdsource. Such crowdsourced live streaming platforms as Twitch.tv allow
general users to broadcast their content to massive viewers, thereby greatly
expanding the content and user bases. The resources available for these
non-professional broadcasters however are limited and unstable, which
potentially impair the streaming quality and viewers' experience. The diverse
live interactions among the broadcasters and viewers can further aggravate the
problem.
In this paper, we present an initial investigation on the modern crowdsourced
live streaming systems. Taking Twitch as a representative, we outline their
inside architecture using both crawled data and captured traffic of local
broadcasters/viewers. Closely examining the access data collected in a
two-month period, we reveal that the view patterns are determined by both
events and broadcasters' sources. Our measurements explore the unique source-
and event-driven views, showing that the current delay strategy on the viewer's
side substantially impacts the viewers' interactive experience, and there is
significant disparity between the long broadcast latency and the short live
messaging latency. On the broadcaster's side, the dynamic uploading capacity is
a critical challenge, which noticeably affects the smoothness of live streaming
for viewers.
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