  Multiview video has recently emerged as a means to improve user experience in
novel multimedia services. We propose a new stochastic model to characterize
the traffic generated by a Multiview Video Coding (MVC) variable bit rate
source. To this aim, we resort to a Poisson Hidden Markov Model (P-HMM), in
which the first (hidden) layer represents the evolution of the video activity
and the second layer represents the frame sizes of the multiple encoded views.
We propose a method for estimating the model parameters in long MVC sequences.
We then present extensive numerical simulations assessing the model's ability
to produce traffic with realistic characteristics for a general class of MVC
sequences. We then extend our framework to network applications where we show
that our model is able to accurately describe the sender and receiver buffers
behavior in MVC transmission. Finally, we derive a model of user behavior for
interactive view selection, which, in conjunction with our traffic model, is
able to accurately predict actual network load in interactive multiview
services.
