  Driven by the tremendous technological advancement of personal devices and
the prevalence of wireless mobile network accesses, the world has witnessed an
explosion in crowdsourced live streaming. Ensuring a better viewers quality of
experience (QoE) is the key to maximize the audiences number and increase
streaming providers' profits. This can be achieved by advocating a
geo-distributed cloud infrastructure to allocate the multimedia resources as
close as possible to viewers, in order to minimize the access delay and video
stalls. Moreover, allocating the exact needed resources beforehand avoids
over-provisioning, which may lead to significant costs by the service
providers. In the contrary, under-provisioning might cause significant delays
to the viewers. In this paper, we introduce a prediction driven resource
allocation framework, to maximize the QoE of viewers and minimize the resource
allocation cost. First, by exploiting the viewers locations available in our
unique dataset, we implement a machine learning model to predict the viewers
number near each geo-distributed cloud site. Second, based on the predicted
results that showed to be close to the actual values, we formulate an
optimization problem to proactively allocate resources at the viewers
proximity. Additionally, we will present a trade-off between the video access
delay and the cost of resource allocation.
