A Poisson Hidden Markov Model for Multiview Video Traffic

TL;DR

This paper introduces a Poisson Hidden Markov Model to accurately characterize multiview video traffic, enabling better network management and user experience prediction in multimedia services.

Contribution

The paper presents a novel P-HMM for multiview video traffic modeling, including parameter estimation, traffic simulation, and application to network buffer and user behavior prediction.

Findings

Model produces realistic traffic characteristics.

Accurately describes buffer behavior in MVC transmission.

Predicts network load in interactive multiview services.

Abstract

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.