Traffic models for H.264 video using hierarchical prediction structures

Abstract

Abstract-We present different video traffic models for H.264 variable bit rate (VBR) videos. We propose our models on top of the recent unified traffic model developed by Dai et al. [1], which presents a frame-level hybrid framework for modeling MPEG-4 and H.264 multi-layer VBR video traffic. We exploit the hierarchical predication structure inherent in H.264 for intraGoP (group of pictures) analysis. We model the children frames by considering various combinations of the correlation between the parent frames in the prediction structure. Our simulations show that modeling using the hierarchical prediction structure indeed improves capturing the statistical features of the videos and prediction of network performance, without an increase in the complexity as compared to the unified traffic model by Dai et al. [1], which was shown earlier to be better than previous traffic models.

Time Series Analysis of H.264 Video

“…Hence a model that can capture both dependencies is needed. The results obtained in Figure ( 9), ( 10) and (11) show that the parameter P k follows a 45 line closely, as an indicator that variances at different scales are equivalent and hence the series shows 2 nd order stationarity.…”

“…The encoded video has 3 frame types as identified by the Moving Pictures Experts Group (MPEG). They are [11]:  I-frame: intra-coded.  P-frame: forward predictive coded with motion compensated prediction from a prior I or P frame.…”

“…It is symmetric but not continuous. Its scaling and wavelet functions are respectively given by equations (10) (11).…”