  Large Internet video delivery systems serve millions of videos to tens of
millions of users on daily basis, via Video-on-Demand and live streaming. Video
popularity evolves over time. It represents the workload, as welll as business
value, of the video to the overall system. The ability to predict video
popularity is very helpful for improving service quality and operating
efficiency. Previous studies adopted simple models for video popularity, or
directly adopted patterns from measurement studies. In this paper, we develop a
stochastic fluid model that tries to capture two hidden processes that give
rise to different patterns of a given video's popularity evolution: the
information spreading process, and the user reaction process. Specifically,
these processes model how the video is recommended to the user, the videos
inherent attractiveness, and users reaction rate, and yield specific popularity
evolution patterns. We then validate our model by matching the predictions of
the model with observed patterns from our collaborator, a large content
provider in China. This model thus gives us the insight to explain the common
and different video popularity evolution patterns and why.
