Smoothing-aided Support Vector Machine based Nonstationary Video Traffic
Prediction Towards B5G Networks
Abstract
Video services have hold a surprising proportion of the whole network
traffic in wireless communication networks. Accurate prediction of video
traffic can endow networks with intelligence in resource management,
especially for the forthcoming beyond the fifth-generation (B5G)
networks. However, the existing approaches fail to accurately predict
video traffic with all types of frames, due to the natures of strong
long-range dependence, self-similarity and burstiness. Obviously, it is
unable to meet the QoS and QoE requirements of dynamic bandwidth
allocation. In this paper, we propose the feasibility of advanced
machine learning methodology applied in nonstationary video traffic
prediction, i.e., smoothing-aided support vector machine (SSVM) model.
The model utilizes classical smoothing methods to preprocess video
traffic by relieving the drastic fluctuation of video stream. It can
provide an effective association for the subsequent support vector
regression, as the preprocessed data becomes more smooth and continuous
than the original unprocessed one. Experimental results show that our
proposed model significantly outperforms the state of the art model,
i.e., logistic smooth transition autoregressive, in prediction
performance. The superior nonlinear approximation capacity is further
demonstrated by visualized statistical analysis.