  Crowdsourced live video streaming (livecast) services such as Facebook Live,
YouNow, Douyu and Twitch are gaining more momentum recently. Allocating the
limited resources in a cost-effective manner while maximizing the Quality of
Service (QoS) through real-time delivery and the provision of the appropriate
representations for all viewers is a challenging problem. In our paper, we
introduce a machine-learning based predictive resource allocation framework for
geo-distributed cloud sites, considering the delay and quality constraints to
guarantee the maximum QoS for viewers and the minimum cost for content
providers. First, we present an offline optimization that decides the required
transcoding resources in distributed regions near the viewers with a trade-off
between the QoS and the overall cost. Second, we use machine learning to build
forecasting models that proactively predict the approximate transcoding
resources to be reserved at each cloud site ahead of time. Finally, we develop
a Greedy Nearest and Cheapest algorithm (GNCA) to perform the resource
allocation of real-time broadcasted videos on the rented resources. Extensive
simulations have shown that GNCA outperforms the state-of-the art resource
allocation approaches for crowdsourced live streaming by achieving more than
20% gain in terms of system cost while serving the viewers with relatively
lower latency.
