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On-line Prediction Algorithm for Non-stationary VBR Traffic  

Kang, Sung-Joo (한국전자통신연구원 디지털홈 연구단)
Won, You-Jip (한양대학교 전자전기컴퓨터공학부)
Seong, Byeong-Chan (중앙대학교 수학통계학부)
Publication Information
Journal of KIISE:Information Networking / v.34, no.3, 2007 , pp. 156-167 More about this Journal
Abstract
In this paper, we develop the model based prediction algorithm for Variable-Bit-Rate(VBR) video traffic with regular Group of Picture(GOP) pattern. We use multiplicative ARIMA process called GOP ARIMA (ARIMA for Group Of Pictures) as a base stochastic model. Kalman Filter based prediction algorithm consists of two process: GOP ARIMA modeling and prediction. In performance study, we produce three video traces (news, drama, sports) and we compare the accuracy of three different prediction schemes: Kalman Filter based prediction, linear prediction, and double exponential smoothing. The proposed prediction algorithm yields superior prediction accuracy than the other two. We also show that confidence interval analysis can effectively detect scene changes of the sample video sequence. The Kalman filter based prediction algorithm proposed in this work makes significant contributions to various aspects of network traffic engineering and resource allocation.
Keywords
VBR Traffic; Traffic Prediction; Kalman Filter; GOP; ARIMA; Scene Change Detection; Multimedia Streaming;
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