TITRE Adaptive Packet Video Streaming Over IP Networks - LaBRI

We run our classification algorithm with the parameters described in the previous section, and
we compute the output of each neuron. We choose the largest neuron output which reflects the
high similarity. We choose also the value of λ
1 k
= λ2k
= λ3k
= 1 which connect each feature from the
vector the network.
The result output of the neuron network is shown is Table 4-3. As said, we choose the largest
output from the network. According to these results we can see that O1 is marked with AF11 PHB
and that O2, O3 are both marked with AF12 PHB. Regarding the execution time, we have
measured the mapping of 100 AVO is about 30 millisecond. The complexity of this algorithm is
Ο ( n
2 ) .
EF AF11 AF12 AF13 Best
O 1 3.96 3.98 4.02 0.70 2.60
O 2 3.90 3.98 4.00 0.69 2.60
O 3 3.81 3.90 3.60 0.62 2.49
Table 4-3: Results output of the RBF network
4.1.3.2 Experimental Results
Regarding the performance measurements, we will interest on the network parameters such
the end-to-end one-way delay encountered by video packet between the server and the destination,
and the packet loss probability for each video object stream. This performance metrics are given
only for comparison purpose between the scenario with classification (scenario 1) and without
classification (scenario 2) model.
Figure 4-7 shows the end-to-end video packet transfer delay in both scenarios. We remark
that the end-to-end transfer delay increases when the amount of video sent by the server increases
(time t=30s). The network load is about 85% (8,5 Mbits). When comparing Figure 4-7 (a) and (b),
we note that using the classification and prioritization mechanism (scenario), we get the maximum
QoS expected. Important streams (AVOs) will be transmitted as soon as possible to the player and
with respect to the QoS required by it. The mean transfer delay is about 114 s in scenario 1 and
120s in scenario 2.
Figure 4-8 enforces this measurement by providing loss ratio. We notice that important video
objects are protected against loss since they are marker with low drop precedence and this in case
of scenario 1. In scenario 2, the loss can affect important object during network congestion
(time=30s). This metric, shows a better protection of relevant video objects in the scene during
transmission and network congestion.
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