Journal of Multivariate Analysis
Source models for VBR broadcast-video traffic
IEEE/ACM Transactions on Networking (TON)
Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Characterization of Slice-Based H.264/AVC Encoded Video Traffic
ECUMN '07 Proceedings of the Fourth European Conference on Universal Multiservice Networks
Analysis of losses in a bufferless transmission link
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
Modeling of dependence in a peer-to-peer video application
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Statistical analysis and modeling of Skype VoIP flows
Computer Communications
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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.