Variable bit rate video traffic prediction based on kernel least mean square method

Nasser Haghighat; Hashem Kalbkhani; Mahrokh G. Shayesteh; Mehdi Nouri

Variable bit rate video traffic prediction based on kernel least mean square method

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Author(s): Nasser Haghighat ¹ ; Hashem Kalbkhani ¹ ; Mahrokh G. Shayesteh ^{1, 2} ; Mehdi Nouri ³
- Affiliations: 1: Department of Electrical Engineering, Urmia University, Urmia, Iran ;
  2: Wireless Research Laboratory, Electrical Engineering Department, ACRI, Sharif University of Technology, Tehran, Iran ;
  3: Department of Electrical Engineering, University of Isfahan, Isfahan, Iran
Source: Volume 9, Issue 9, September 2015, p. 777 – 794
DOI: 10.1049/iet-ipr.2014.1035 , Print ISSN 1751-9659, Online ISSN 1751-9667

Received 01/07/2014, Accepted 31/03/2015, Revised 10/03/2015, Published 01/09/2015

In this study, the problem of variable bit rate (VBR) video traffic prediction is addressed. VBR traffic prediction is necessary in dynamic bandwidth allocation for multimedia quality of service control strategies. Autoregressive (AR) models have been widely used in VBR traffic prediction where the least mean square (LMS)-based methods were utilised for parameter estimation. However, they are ineffective when the traffic is dynamic in nature. In this study, using the Brock, Dechert, and Scheinkman (BDS) test, it is shown that the video traffic is non-linear. Kernel is an efficient tool to convert non-linear data into linear one in a higher-dimensional space. The kernel LMS (KLMS) method is proposed to forecast the next frame sizes of I, B and P frames as well as the next group-of-pictures (GOP) size of video traffic. Extensive simulations were performed on different video traces where different performance metrics were considered. KLMS results were very close to those of the Wiener–Hopf optimum solution and better than the results of commonly used normalised LMS and other algorithms such as the least mean kurtosis (LMK), wavelet LMK, adaptive network fuzzy inference system (ANFIS) and neural networks.

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Variable bit rate video traffic prediction based on kernel least mean square method

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