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A rate allocation is said to be -proportional fair, if for any feasible allocation (15).
Solving this optimization problem leads to a rate allocation that can be feasibly scheduled, as shown later on.
A rate allocation that maximizes the inner problem can be calculated directly for a given independently of and.
In our rate allocation paradigm, we have two objectives: find a rate allocation strategy to maximize the total utility and prolong the networks lifetime.
Hence, Zhu et al. propose a rate allocation scheme to optimize the expected received video quality based on simple models of encoder rate-distortion performance and network rate-congestion trade-offs [9].
In particular, with Linear Programming (LP) and Convex Programming (CP), we seek a rate allocation along with a flow allocation and a transmission schedule such that the network throughput can be maximized and the desired fairness can be achieved.
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We answer the question: Given sources that react predictably to congestion notification, can we enforce a desired rate allocation through a single centralized controller?
It has been seen that such a uniform rate allocation results in a performance degradation of around 5 dB in terms of SINR compared to a nonuniform allocation obtained using a greedy optimization approach.
In a greedy rate allocation scheme, at each iteration, one bit is allocated to the band where the additional bit results in the largest decrease in distortion.
Instead of a time-consuming optimization process, we further enhance LSPIHT with a bit rate allocation mechanism based on perceptual factors to distribute the available bandwidth among different channels considering relative visual impacts on the rendered motion quality.
In [5], Morbée et al. propose an encoder rate allocation algorithm for a Stanford-like pixel domain WZ video codec.
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