  Current works on Information Centric Networking assume the spectrum of
caching strategies under the Least Recently/ Frequently Used (LRFU) scheme as
the de-facto standard, due to the ease of implementation and easier analysis of
such strategies. In this paper we predict the popularity distribution of
YouTube videos within a campus network. We explore two broad approaches in
predicting the popularity of videos in the network: consensus approaches based
on aggregate behavior in the network, and social approaches based on the
information diffusion over an implicit network. We measure the performance of
our approaches under a simple caching framework by picking the k most popular
videos according to our predicted distribution and calculating the hit rate on
the cache. We develop our approach by first incorporating video inter-arrival
time (based on the power-law distribution governing the transmission time
between two receivers of the same message in scale-free networks) to the
baseline (LRFU), then combining with an information diffusion model over the
inferred latent social graph that governs diffusion of videos in the network.
We apply techniques from latent social network inference to learn the sharing
probabilities between users in the network and apply a virus propagation model
borrowed from mathematical epidemiology to estimate the number of times a video
will be accessed in the future. Our approach gives rise to a 14% hit rate
improvement over the baseline.
