Classification of slice-based VBR video traffic and estimation of link loss by exceedance |
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| Authors: | Natalia M Markovich Astrid Undheim Peder J Emstad |
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| Affiliation: | 1. Institute of Control Sciences, Russian Academy of Sciences, Profsoyuznay 65, 117997 Moscow, Russia;1. Kitano Research Center of Mental Neurological, Sensory and Motor Organ Disorders, Osaka 530-8480, Japan;2. Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan;3. Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan;4. Department of Neurology and Neuroscience, Graduate School of Medical Sciences, Nagoya City University, Nagoya 467-8602, Japan;5. Emeritus Professor of Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan;1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran;2. Computer Engineering Department, Islamic Azad University, North Tehran Branch, Tehran, Iran;3. Computer Engineering Department, Urmia University, Urmia, Iran;4. Iran Telecommunication Research Center, ITRC, Tehran, Iran;1. Dept. of Software Convergence Technology, Ajou University, San 5 Wonchon-dong Youngtong-gu, Suwon, Gyeonggi-Do 443-749, South Korea;2. Dept. of Computer Engineering, Graduate School, Ajou University, Suwon 443-749, South Korea;3. IP Service Team, Korea Institute of Patent Information, Seoul 146-8, South Korea |
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| Abstract: | Classification of a video stream is an essential preliminary step to estimate the bit loss when the video stream is transmitted over a communication network. In this paper, we classify the video frames by the average frame size and estimate the bit loss for each class when the bitrate exceeds the capacity of the bottleneck link. The video stream under study is encoded using the explicit slice-based H.264/AVC encoding scheme. This scheme reduces the burstiness of regular H.264/AVC encoded video by removing the traditional GOP structure. Instead, a repetitive combination of intracoded and predicted slices is employed, thereby introducing a specific dependence structure in the video data. We consider a bufferless model of the communication system and evaluate the channel capacity required to give a maximum allowed loss rate for each class.Due to the high variability, non-stationarity and non-homogeneity of the underlying video data, the obtained classes are checked regarding the dependence and distribution structure of the data. The high quantiles of the losses are estimated for each class. |
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