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In order to do that, first the TF parameters modified for reducing the bit rates have to be expanded back to their original forms.
By reducing the bit rate by 20% during transmission, we can stream video at much lower transport bandwidth under loaded conditions, by adding a virtual memory framework.
In addition, the resulting density can be further boomed by reducing the bit pitch that is determined by the rim size of the written bit [17].
Let x n be the digital signal that results from the quantization of x ~ n with a fixed-rate uniform quantizer of B bits/sample and step size: Δ = A max - A min / 2 B. The easiest way of reducing the bit rate of x ~ n is to remove the l-least significant bits (LSBs) of each codeword of x ~ n.
Since the HFR method enables a reduction of the core coder bandwidth and the HFR technique requires significantly lower bit rate to code the high-frequency range than a waveform coder would, a coding gain can be achieved by reducing the bit rate allocated to the waveform core coder while maintaining full audio bandwidth.
The larger value of (P_{k_{textit {overflow}}}^{t+T}) means that queue overflow is more likely to occur and the corresponding user queue should have the higher priority of resource allocation, thus reducing the bit loss rate and satisfying QoS requirement.
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However, it is a relatively simple, non-invertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity.
The output of the block is configured to rounded quantization to reduce the bit latency.
Fitting a curve to the energy parameter further reduces the bit rate [25, 26].
It significantly reduces the bit rates required at mobile terminals, thus conserving the wireless resources.
Numerical results show that the proposed schemes can reduce the bit error rate (BER) significantly.
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