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The jitter probability of multimedia stream is denoted by the cooperative control probability of video frames of mobile multimedia crowd service.
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Nevertheless, this limit is very hard if not impossible to compute due to the diversity and complexity of probability models of multimedia contents.
Similarly, the failure probability of a multimedia mobile node is shown in the formula (12).
Simulation analysis shows that XL-WMSN increases the probability of delivering multimedia content within their allocated deadline and is more efficient than existing solutions.
The packets-loss probability of the aggregated multimedia source is computed as (14).
The probability of distortion and compensation of multimedia static communication flows is fixed.
Compared to traditional wireless sensor networks, the probability of congestion occurrence in wireless multimedia sensor networks is higher due to the high volume of data arising from multimedia streaming.
The packets-loss probability of the aggregated video sources, multimedia source, www source and ftp sources used as the threshold to control the external traffic arriving into the queue is shown in Figure 7.
This is a high-probability-of-failure mission.
Based on cooperative neural network and opportunity Markov chain model, the accurate prediction of multimedia mobile sensor node state and state transition probability then is given based on mobility prediction of dynamic topology evolution mechanism and the opportunity computational scheme.
The proposed scheme is applied to heuristically find resource allocations, which maximize the quality of security and the probability of meeting deadlines for all the multimedia applications running on heterogeneous networks.
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