In the same way as with Huffman codes, shorter codewords can be assigned to symbols with higher probabilities and vice versa. Then, dependent Lagrangian optimization is used to determine the overall bit allocation across all layers. This book is therefore written to be accessible to researchers, expert industrial R engineers, and university students who are interested in the cutting edge technologies in joint source-channel video transmission. Our problem is to allocate the available bit rate across scalable layers and, within each layer, between source and channel coding, while minimizing the end-to-end distortion of the received video sequence. The first few unary codes are shown in Table 7. Then, dependent Lagrangian optimization is used to determine the overall bit allocation across all layers.
Computer simulations are performed to show the advantages of the proposed model. Further, any remaining redundancy, after compression, can be used at the receiver for a post-decoding cleanup phase, called error concealment in the literature. On Joint Source-Channel Coding -- 3. We derive a method to optimally estimate the overall distortion at the decoder. Computer simulations are performed to show the advantages of the proposed model. The optimization is done in an integrated manner and in one step. The optimization algorithm utilizes rate-distortion characteristic plots.
The joint source-channel coding approach aims to efficiently perform content-aware crosslayer resource allocation, thus increasing the communication efficiency of multiple network layers. Not only does this corrupt the corresponding data but the following code words, even if decoded to the correct values, will have a positional error. Joint Source Coding and Forward Error Correction -- 6. Multimedia data for example typically contain components with different perceptual significance and. A single bit in error can make one transmitted code appear to be two shorter ones at the decoder or vice versa.
Besides the compression capabilities, the Mojette transforms brings an interesting property: a tunable redundancy. Slice lengths are designed based on efficiency, burst length, and preferred network packet or cell size. The optimization is done in an integrated manner and in one step. Then, dependent Lagrangian optimization is used to determine the overall bit allocation across all layers. These plots show the contribution of each layer to the total distortion as a function of the source rate of the layer and the residual bit error rate the error rate that remains after the use of channel coding. Pappas and Randall Berry and Aggelos K.
The ability to respond to variations in the available bandwidth i. Joint Source Coding and Power Adaptation -- 7. In addition, the major research challenges in the field are demonstrated and discussed, which include power-management for mobile devices, rate-distortion-complexity optimized video codec design, and computational complexity and power aware cross-layer design and optimization. These solutions, however, neglect that the environmental dynamics are also affected by the cross-layer transmission strategies, thereby affecting the future utility derived by the network users. The optimization is done in an integrated manner and in one step.
However, such an assumption generally requires an ideal channel coding scheme, such that error-free transmission of the source output over a noisy channel with source bit rate R D less than the channel capacity can be guaranteed. This book deals with the problem of joint source-channel video transmission, i. Joint Source-Channel Coding -- 3. As we have seen, the various coding standards place data into slices or groups of blocks that terminate with a sync word, which prevents the error propagation across this boundary. In using joint source-channel decoding, the channel decoder does not make hard decisions on the output X ˆ s. We derive a method to optimally estimate the overall distortion at the decoder.
Ali Grami, in , 2016 10. Channel Modeling and Channel Coding -- 5. The plots are obtained experimentally using representative video sequences and show the sensitivity of the source encoder and decoder to channel errors. It provides not only an optimization benchmark against which the performance of other suboptimal systems can be evaluated, but also a useful tool for assessing the effectiveness of different error control components in practical system design. The model uses one of the possible combinations of matrix parameters N, R, and P and the permuted random matrices R x. These plots are used in conjunction with plots that show the bit error rate achieved by the allowable channel coding schemes for given channel conditions in order to obtain the operational rate-distortion curve of each layer. Description: xii, 136 pages : illustrations ; 24 cm.
However, most existing mobile devices are powered by battery with limited energy resource. The plots are obtained experimentally using representative video sequences and show the sensitivity of the source encoder and decoder to channel errors. In other words, it depends on how to jointly select video source parameters and channel parameters based on the video content characteristics, available network resources, and underlying network conditions, given the fact that the power management schemes as well as the nonlinear battery effects also affect the system power consumption of mobile devices. Additionally, when compressed video is sent over a channel or network, it must be made robust with respect to much more frequent errors and data loss. Our goal is to limit the amount of distortion in the received video sequence, while minimizing transmission energy. This strategy generally will require a larger output buffer and may not be suitable for real-time communication.