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Download Bandwidth Allocation for Video under Quality of Service by Bushra Anjum, Harry G. Perros PDF

By Bushra Anjum, Harry G. Perros

We current queueing-based algorithms to calculate the bandwidth required for a video movement in order that the 3 major caliber of carrier constraints, i.e., end-to-end hold up, jitter and packet loss, are ensured.

Conversational and streaming video-based purposes have gotten an immense a part of the standard net utilization. the standard of those purposes (QoS), as skilled by means of the consumer, is determined by 3 major metrics of the underlying community, specifically, end-to-end hold up, jitter and packet loss. those metrics are, in flip, without delay with regards to the potential of the hyperlinks that the video site visitors traverses from its resource to vacation spot. the most challenge that this publication addresses is how a lot bandwidth we should always allocate at the course from resource to vacation spot of a video site visitors move such that the end-to-end hold up, jitter and packet lack of the video packets are inside of a few anticipated required bounds.

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How to achieve this is the issue addressed in this chapter. This chapter is organized as follows. In the next section, we give examples where an expression for adding percentiles can be useful. 2, we present an expression for adding up percentiles of random variables that are exponentially distributed with either the same or different rates. The results are then extended to the more general Coxian-2 distribution. 3. 4, we provide an example of calculating the shortest path using Dijkstra’s algorithm that minimizes the total percentile of a 2 Bandwidth Allocation for Video under Quality of Service Constraints performance metric, such as delay, energy, jitter and power attenuation of a signal.

If the moment generating function of the delay in one node can be written as: Dn(s) = Hn(s)/(s − pn), where s − pn is the dominant pole, the endto-end distribution is a weighted sum of the Cumulative Density Function (CDF) of Erlang variables. Experimental evidence shows that the second and third approaches outperform the Gaussian approximation in all cases, the main reason of this is that a very large number of nodes (order of a few hundred) are required to make the resultant cdf a Gaussian distribution.

Simulations illustrate good accuracy of the closed-form expressions. Iftikhar et al. [IFT 06] present an analytical model based on G/M/1 queuing system for modeling of wireless IP traffic behavior under the assumption of four different classes of self-similar traffic per single router. The model implements four queues based on G/M/1 queuing system and it was analyzed on the basis of priority with no xxxii Bandwidth Allocation for Video under Quality of Service Constraints preemption. The first major contribution of the paper is to calculate the packet interarrival time distribution for a particular self-similar traffic.

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