@inproceedings{tanwir_nayak_perros_2016, title={Modeling 3D video traffic using a Markov Modulated Gamma Process}, DOI={10.1109/iccnc.2016.7440638}, abstractNote={Three-dimensional (3D) television and video streaming has become very popular over the last few years. Multiview encoded video is used to support 3D video applications. The statistical characteristics of multiview video are significantly different from the traditional single-view video and therefore existing video traffic models are no more applicable for this type of video. In this paper, we propose and evaluate a new model for multiview video that is based on a Markov process. To the best of our knowledge, there is only one other model for multiview video that has been proposed in the literature by Rossi et al. We compared the two models using Q-Q plots and the autocorrelation function of the frame sizes along with QoS metrics of the resulting packet traces estimated by simulation. The comparison results show that our model has less complexity and better accuracy.}, booktitle={2016 International Conference on Computing, Networking and Communications (ICNC)}, author={Tanwir, S. and Nayak, D. and Perros, H.}, year={2016} }
 @article{tanwir_perros_2016, title={Modeling live adaptive streaming over HTTP}, volume={85}, ISSN={["1873-703X"]}, DOI={10.1016/j.comcom.2016.03.025}, abstractNote={Video streaming methods have evolved greatly over the years. Today, the most prevalent technique to stream live and video on-demand is the adaptive HTTP streaming and is used by several commercial vendors. In this paper, we present an approximate analytic model for live adaptive streaming over HTTP. Using this model, we propose a new rate control algorithm that makes the rate transitions less frequent and increases the quality of experience for the viewer. Also, the model can be used to characterize the departure packet process at the video server. To the best of our knowledge, this is the first video traffic model for adaptive HTTP streaming to be reported in the literature.}, journal={COMPUTER COMMUNICATIONS}, author={Tanwir, Savera and Perros, Harry}, year={2016}, month={Jul}, pages={74–88} }
 @book{tanwir_perros_2014, title={VBR Video Traffic Models}, ISBN={["978-1-84821-636-5"]}, ISSN={["2051-249X"]}, DOI={10.1002/9781118931066}, journal={VBR VIDEO TRAFFIC MODELS}, author={Tanwir, S and Perros, H}, year={2014}, pages={1–148} }
 @book{tanwir_2014, title={VBR video traffic models}, publisher={Hoboken, NJ: John Wiley & Sons, Inc.}, author={Tanwir, S.}, year={2014} }
 @article{tanwir_perros_2013, title={A Survey of VBR Video Traffic Models}, volume={15}, ISSN={["1553-877X"]}, DOI={10.1109/surv.2013.010413.00071}, abstractNote={We have seen a phenomenal growth in video applications in the past few years. An accurate traffic model of VBR video is necessary for performance evaluation of a network design and also for creating synthetic loads that can be used for benchmarking a network. In view of this, various models for VBR video traffic have been proposed in the literature. In this paper, we classify and survey these models. In addition, we implemented four representative video traffic models and compared them using the H.264 AVC video traces available at the Arizona State University video traces library. These models are: the Markov Modulated Gamma (MMG) model, the Discrete Autoregressive (DAR) model, the second order Autoregressive AR(2) model, and a wavelet-based model. The results show that the MMG and the wavelet-based models are suitable for both video conference and IPTV, while the DAR model is good for video conference traffic only. According to our results, the AR(2) model is not suitable for generating any type of H.264 video. A brief overview of SVC, HD, and 3D video is also provided.}, number={4}, journal={IEEE COMMUNICATIONS SURVEYS AND TUTORIALS}, author={Tanwir, Savera and Perros, Harry}, year={2013}, pages={1778–1802} }