  The demand for global video has been burgeoning across industries. With the
expansion and improvement of video-streaming services, cloud-based video is
evolving into a necessary feature of any successful business for reaching
internal and external audiences. This paper considers video streaming over
distributed systems where the video segments are encoded using an erasure code
for better reliability thus being the first work to our best knowledge that
considers video streaming over erasure-coded distributed cloud systems. The
download time of each coded chunk of each video segment is characterized and
ordered statistics over the choice of the erasure-coded chunks is used to
obtain the playback time of different video segments. Using the playback times,
bounds on the moment generating function on the stall duration is used to bound
the mean stall duration. Moment generating function based bounds on the ordered
statistics are also used to bound the stall duration tail probability which
determines the probability that the stall time is greater than a pre-defined
number. These two metrics, mean stall duration and the stall duration tail
probability, are important quality of experience (QoE) measures for the end
users. Based on these metrics, we formulate an optimization problem to jointly
minimize the convex combination of both the QoE metrics averaged over all
requests over the placement and access of the video content. The non-convex
problem is solved using an efficient iterative algorithm. Numerical results
show significant improvement in QoE metrics for cloud-based video as compared
to the considered baselines.
