No abstract

The diversity of multimedia-enabled devices supporting streamed multimedia is ever growing. Multicast delivery of TV channels in IP networks to a heterogeneous set of clients can be organised in many different ways, which brings up the discussion which one is optimal. Scalable video streaming has been believed to be more efficient in terms of network capacity utilisation than simulcast video delivery because one flow can serve all terminals, while with simulcast all resolutions are offered in parallel. At the same time, it is also largely recognised that in order to provide the same video quality compared to non-layered video coding, scalable video coding (SVC) incurs a bit rate penalty.In this paper we compare simulcast and SVC in terms of their required capacity in an IPTV network scenario where a bouquet of TV channels is offered to the subscribers. We develop methods to calculate and approximate the capacity demand for two different subscriber behaviour models. These methods are then used to explore the influence of various parameters: the SVC bit rate penalty, the number of offered channels, the channel popularity and the number of subscribers. The main contribution of this paper is that we derive an analytical formula to calculate the SVC limit bit rate penalty beyond which SVC is less efficient than simulcast. In the realistic IPTV examples considered here, the limit is found to lie between 16% and 20%, while the reported values for this coding penalty range from 10% up to 30% for current H.264 SVC codecs, indicating that SVC in IPTV is not always more efficient than simulcast.

We compare the adequacy of two models for realistic video sources, namely the fractional Brownian motion (fBm) and Markov modulated fluid flow (Mmff) models. We use the effective bandwidth approach to get the probability that the buffer content exceeds a certain threshold. We use a formula in which the variance function, i.e., the variance of the traffic arriving in an interval of length τ , plays a central role and we model it as variance associated either with the Mmff or fBm model.We measure the variance function for an artificial source we construct, using Variable Bit Rate (VBR) H.264/AVC (Advanced Video Codec) video traces of real movies. There is a good correspondence between the buffer threshold exceeding probability obtained via trace-based simulations and the one predicted theoretically, based on the measured variance function. These we take as benchmark results against which we check both models-fBm and Mmff.First, we try to tune the model parameters such that their variance function matches the measured one over a large range of τ values, but this proves to be difficult. When matching the variance function over a short range of the τ of interest, we conclude that the Mmff model is better suited to model VBR video sources. We conduct an error sensitivity analysis with non-optimal model parameters and conclude that they do not influence considerably the buffer exceeding probability.Most reliable results are achieved with the measured variance function; if by some reason it needs to be modelled, the Mmff is preferred over the fBm model.

No abstract

No abstract