3.5 Experimental Results
The proposed QoS mapping framework is evaluated by simulations in several steps. For video streaming, an error-resilient version of the ITU-T H.263+ stream is used and decoded by an error-robust video decoder. All other components are then simulated by NS .
3.5.1 Experimental Setting Through the Two-State Markov Model for the Ideal Case
We evaluated simple and online-capable RPI (exactly RLI) in Section 3.3 through an ideal setting in which each DS level q had a different but exact packet loss rate pre-assigned by a given manipulated packet loss curve versus q. Each packet loss rate of q randomly experiences a packet loss rate from the state of the two-state Markov chain known as the Gilbert model, shown in Figure 3.15. The transition probabilities from “Bad” (i.e., packet loss) to “Good” as q = 0.9 and give a little bit of burst to the packet losses. The average packet loss rate varies between 0% and 20%.
The Markov model is popular for capturing temporal loss dependency , , . It is simple to understand and implement in monitoring applications. In Figure 3.15, p is the probability that the next packet will be lost, provided the previous one has arrived. q is the opposite. 1 – q is the conditional loss probability. Normally, p + q < 1. If p + q = 1, the Gilbert model reduces to a Bernoulli model.
Figure 3.15. Network topology model for the simulation.
3.5.2 Performance of Optimal QoS Mapping in the Ideal Case
First, the proposed RPI performance is evaluated with H.263+ video streams under the ideal network condition of Figure 3.12(b). The result shows the effectiveness of packet-/session-based differentiation (performed by QoS mapping) over several typical video sequences. The idealized packet loss rate for each DS level is simulated by a two-state Markov model known as the Gilbert model with transition probabilities from “Bad” (i.e., packet loss) to “Good” as q = 0.9 (a somewhat large value), which gives slightly bursty packet losses. If we set q as a small probability value, it causes relatively longer bursty packet loss (i.e., a large loss-correlation effect).
The phenomenon of bursty packet loss is found easily, but the bursty loss period depends on various network load conditions. A result  shows that a short bursty loss causes a greater extent of video degradation in comparison with a long bursty loss since the former occurs more frequently than the latter under the same packet loss probability. In this experiment, we wanted to show the performance comparison in the case of short bursty packet loss under severe network load conditions.
Figure 3.16 shows the performance comparison between RPI-aware and RPI-blind mapping under total cost constraint. The loss rate and cost curve per DS level are assumed as the curves for pq(i) and shown in Figure 3.12(b). It is interesting to note that the gain in PSNR varies somewhat depending on the video sequences. For “Akiyo,” the gain is smaller than with other sequences, which is partly due to the small movement involved in this scene. However, for other sequences, we can observe a clear advantage of packet-based differentiation. Overall, this result verifies the obvious benefit of RPI-aware QoS mapping in DiffServ networks.
Figure 3.16. Average objective video quality (expressed in PSNR) for several video sequences under the loss/cost assumption of Figure 3.12(b).