A generalized seasonal persistent model for video traffic
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hdl:2117/6379
Document typeConference lecture
Defense date2009-09
PublisherSpringer Berlin / Heidelberg
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Abstract
Video tra±c generated with modern codecs is known to possess a complex statistical structure that includes short memory, long
memory, and seasonal components. We propose a k-factor Generalized Autoregressive Moving Average (k-GARMA) process as a versatile model for video tra±c. We provide a wavelet-based approximate Maximum Likelihood Estimator for such a process, together with some results obtained with H.263 and MPEG-2 video traces.
CitationZuraniewski, P.; Rincón Rivera, D. A generalized seasonal persistent model for video traffic. A: EUNICE 2009 - The Internet of the Future. "The Internet of the Future - Proceedings of EUNICE 2009". Springer Berlin / Heidelberg, 2009, ISBN 978-3-642-03699-6.
ISBN978-3-642-03699-6
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