Joint source and space-time block coding for MIMO video communications

Highly efficient and robust source coding, channel coding, and packetization techniques are proposed for video streaming over wireless ATM. At the base station, which is located at the boundary of the wireline and wireless networks, video received from the wireline source is dynamically transformed to match both the hardware capabilities of mobile hosts and the time-varying wireless channel conditions. For wireless transmission, source coding, channel coding, and packetization are jointly implemented as part of the application. For source coding, we propose to use our three-dimensional significance-linked connected component analysis video codec. For channel coding and packetization, both intracell and interlaced (intercell) forward error correction are applied. Furthermore, the time-varying channel characteristics is exploited by adaptively allocating the total bit budget between source coding and channel coding. Extensive performance evaluation demonstrates the effectiveness of the proposed wireless video streaming technique.

This paper investigates the tradeoffs of source coding, channel coding and spreading in CDMA systems. We consider a system consisting of an image source coder, a convolutional channel coder, an interleaver, and a direct sequence spreading module. With different allocations of bandwidth to source coding, channel coding and spreading, the system is analyzed over a frequency selective Rayleigh fading channel. The performance of the system is evaluated using the cumulative distribution function of peak signal-to-noise ratio. Tradeoffs of different components of the system are determined through simulations. We show that, for a given bandwidth, an optimal allocation of that bandwidth can be found. Tradeoffs among the parameters allow us to tune the system performance to specific requirements.

We propose an iterative design algorithm for jointly optimizing source and channel codes. The joint design combines channel-optimized vector quantization (COVQ) for the source code with rate-compatible punctured convolutional (RCPC) coding for the channel code. Our objective is to minimize the average end-to-end distortion. For a given channel SNR and transmission rate, our joint source and channel code design achieves an optimal allocation of bits between the source and channel coders. This optimal allocation can reduce distortion by up to 6 dB over suboptimal allocations for the source data set considered. We also compare the distortion of our joint iterative design with that of two suboptimal design techniques: COVQ optimized for a given channel bit-error-probability, and RCPC channel coding optimized for a given vector quantizer. We conclude by relaxing the fixed transmission rate constraint and jointly optimizing the transmission rate, source code, and channel code.

An improved MIMO system implementing joint source and channel coding in the form of Rate Compatible Punctured Convolutional Code (RCPC Code) and Unequal Error Protection (UEP) is proposed. To ensure a multiplexing gain, a layered architecture, V-BLAST, is employed. Analytical BER is presented and numerical simulations are done to analyze the system performance. The proposed coding scheme allows for consistently higher error-protection level as the code rate decreases. It is also shown that whilst the lower code rates outperforms the higher ones, in terms of larger free distance and lower BER, the SNR range covered is narrower at decreasing levels of BER. This implies the potential bandwidth efficiency to be gained if the higher code rates are used for lower-priority data bits.

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.

Introduction In this chapter we present the applicability of probability theory and random variables to the formulation of information theory pioneered by Claude Shannon in the late 1940s. Information theory introduces the general idea of source coding and channel coding . The purpose of source coding is to minimize the bit rate required to represent the source (represented mathematically by a discrete random variable) with a specified efficiency at the output of the source coder. On the other hand, the goal of channel coding is to maximize the bit rate at the input of the channel encoder so that code words can be transmitted through the channel with a specified reliability. Both source coding and channel coding can be achieved with the knowledge of the statistics of both the source and channel. Entropy of a discrete source Information comes from observing the outcome of an event. Common events occur frequently (high probability) and therefore carry little information. On the other hand, rare events occur infrequently (low probability) and hence carry much more information. In 1928 R. V. L. Hartley proposed a logarithmic measure of information that illustrates this observation.

A novel joint source and channel coding scheme tailored to 3D video is proposed in this paper to minimize the end-to-end view synthesis distortion within a given total bit rate for both texture and depth as well as a maximum tolerable distortion constraint for texture. First, we formulate a joint texture and depth coding mode selection strategy for error-resilient source coding of multi-view video plus depth-based 3D video through using the Lagrange multiplier method. Then, by considering the effect of residual errors after channel coding, we evolve to a more general formulation that jointly optimizes error-resilient source coding and channel coding in an integrated manner for unequal error protection between texture and depth, for which a theoretic solution using a proposed dual-trellis is derived. Finally, we extend the general formulation by including the texture distortion constraint. We show how to optimize the view synthesis quality while simultaneously catering to the texture quality constraint. Experimental results demonstrate the proposed algorithm has much better performance than existing related work.

High reliable and efficient image transmission is of primary importance for space communications. In the traditional space image transmission system design, the source coding module and channel coding module are separated. This separate design, although simple to be implemented, cannot explore the transmission performance to the most. In this article, we propose a joint source–channel rate allocation framework to improve the space image transmission performance. A joint rate allocation algorithm based on the packet loss rate is proposed. For certain required system transmission rate, the optimal source coding and channel coding rate pair can be selected from pre-configured code rate sets by sliding search. In addition, our design has taken into account the progressive scalability feature of the image compression results. Each of the source coding output packets is divided into several sub-packets with different levels of significance and then channel coding with different rates is applied on these sub-packets to achieve unequal error protection. Simulation results show that the proposed joint design can significantly improve the image reconstruction quality. Compared with the traditional separate design, the proposed joint design can achieve 3–5 dB performance gain in terms of peak signal-to-noise ratio.

The problem of application-layer error control for real-time video transmission over packet lossy networks is commonly addressed by joint source-channel coding (JSCC). The traditional JSCC approaches solve this problem in a sequential manner, where source coding and channel coding are not fully integrated. In this paper, we present an integrated joint source-channel coding (IJSCC) framework, where error resilient source coding, channel coding and error concealment are jointly considered in an integrated manner. We show through both analysis and simulations the advantages of the proposed IJSCC approach, in comparison to a sequential JSCC approach.

We propose a general framework based in the Gilbert model for cross-layer analysis of TCP and UDP over MIMO wireless systems. Our framework takes into consideration diverse system characteristics often difficult to express as a Gilbert model such as fading, space-time transmission schemes, modulation, channel coding and ARQ. We apply our framework to analyze the TCP performance of two representative MIMO systems, namely, the BLAST system and the orthogonal space-time block coded (STBC) system. In particular, we investigate the optimal information rate that maximizes the TCP throughput, the effect of Doppler on the optimal TCP throughput and the optimal channel coding rate for various modulations. We provide simulations results from the ns-2 network simulator to demonstrate the accuracy of the proposed analytical framework in characterizing the TCP performance. We further apply the framework to two additional cross-layer applications: the analysis of the buffer occupancy on the base station, and the analysis of CBR video transmission over MIMO systems. We show that while the optimal rate for maximum TCP throughput is far from the channel capacity, the optimal rate for error and delay-tolerant video transmission requires much higher rates, and so the physical layer should be aware and adapt to the type of application in order to increase the system performance. We also show that mobility benefits systems with larger buffers, especially for TCP, as the ARQ scheme is able to recover the shorter burst errors. In general, our investigation shows that the type of application plays a crucial role in the optimization of a wireless system, and that our modelling framework is useful for the cross-layer analysis and design of those systems.

Scalable video coding technique has been desired for many years to fulfill the requirement of reliable transmission of video over heterogeneous networks. It allows for simple solutions in adaptation to network constraint and user capabilities by providing a full scalability including temporal, spatial and quality scalability. Various coding structures will dramatically affect the overall performance of the system. In this paper, we develop a new joint source and channel coding scheme for wireless broadcasting making use of H.264/SVC considering the various clients' statistics such as the accessing bandwidth and the error rate. First, an effective algorithm is carried out to distribute proper amount of source codes and channel codes to the base layer to guarantee that all the clients can receive the video with a minimum quality. Second, to deal with the burst packet loss, we propose to apply the unequal error protection (UEP) scheme on the enhancement layers using the interleaved packetization to make sure that all the lower layers can be decoded if an enhancement layer is correctly received. To maximize the system-defined utility function, fast algorithms are designed to dynamically allocate the source coding rate and the channel coding rate for the scalable bit-stream given the clients' distribution. Experiments are conducted to verify the performance of the proposed algorithm against the other two schemes, where equal error protection (EEP) and UEP schemes with different coding structures are employed. The results demonstrate the superiority of the proposed scheme and the improvement is up to 2 dB.

Multimedia encoders typically generate symbols having a wide range of legitimate values. In practical mobile wireless scenarios, the transmission of these symbols is required to be bandwidth efficient and error resilient, motivating both source coding and channel coding. However, separate source and channel coding (SSCC) schemes are typically unable to exploit the residual redundancy in the source symbols, which cannot be totally reduced by finite-delay finite-complexity schemes, hence resulting in a capacity loss. Until recently, none of the existing joint source and channel codes (JSCCs) were suitable for this application since their decoding complexity rapidly increases with the size of the symbol alphabet. Motivated by this, we proposed a novel JSCC referred to as the unary error correction (UEC) code, which is capable of exploiting all residual redundancy and eliminating any capacity loss, while imposing only a moderate decoding complexity. In this paper, we show that the operation of the UEC decoder can be dynamically adapted to strike an attractive tradeoff between its decoding complexity and its error correction capability. Furthermore, we conceive the corresponding 3-D EXtrinsic Information Transfer (EXIT) charts for controlling this dynamic adaptation, as well as the decoder activation order, when the UEC code is serially concatenated with a turbo code. In this way, we expedite iterative decoding convergence, facilitating a gain of up to 1.2 dB compared with both SSCC and to its nonadaptive UEC benchmarkers, while maintaining the same transmission bandwidth, duration, energy, and decoding complexity.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper examines near-capacity dirty-paper code designs based on source–channel coding. We first point out that the performance loss in signal-to-noise ratio (SNR) in our code designs can be broken into the sum of the packing loss from channel coding and a modulo loss, which is a function of the granular loss from source coding and the target dirty-paper coding rate (or SNR). We then examine practical designs by combining trellis-coded quantization (TCQ) with both systematic and nonsystematic irregular repeat–accumulate (IRA) codes. Like previous approaches, we exploit the extrinsic information transfer (EXIT) chart technique for capacity-approaching IRA code design; but unlike previous approaches, we emphasize the role of strong source coding to achieve as much granular gain as possible using TCQ. Instead of systematic doping, we employ two relatively shifted TCQ codebooks, where the shift is optimized (via tuning the EXIT charts) to facilitate the IRA code design. Our designs synergistically combine TCQ with IRA codes so that they work together as well as they do individually. By bringing together TCQ (the best quantizer from the source coding community) and EXIT chart-based IRA code designs (the best from the channel coding community), we are able to approach the theoretical limit of dirty-paper coding. For example, at 0.25 bit per symbol (b/s), our best code design (with 2048-state TCQ) performs only 0.630 dB away from the Shannon capacity. </para>

In this paper, we present an unequal error protection method for packet-loss resilient transmission of progressively compressed 3D meshes. The proposed method is based on a source and channel coding approach where we set up a theoretical framework for the overall system by which the channel packet loss behavior and the channel bandwidth can be directly related to the decoded mesh quality at the receiver. In particular, we develop a statistical distortion measure and optimize it to compute the best combination of (i) the number of triangles to transmit, (ii) the total number of channel coding bits, and (iii) the distribution of these error-protection bits among the transmitted layers in order to maximize the expected decoded mesh quality at the receiver. The proposed method differs from the earlier approaches in two major aspects: (i) determination of the number of channel coding bits (C) and (ii) the approach of reducing the source rate in order to accommodate for channel coding bits. When the proposed method is used to transmit a typical 3D mesh over a channel with a 10% packet loss rate, the distortion (measured using the Hausdorff distance between the original and the decoded overly sampled meshes) is reduced by 50% compared to the case when no error protection is applied.

Joint source and channel coding (JSCC) has attracted increasing attention in semantic communications. However, JSCC is vulnerable to privacy issues due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-protective JSCC (DPJSCC) for image transmission, which aims at protecting private information and achieving superior image transmission performance. In particular, we propose a disentangled information bottleneck objective to compress the private information in public subcodewords and improve the reconstruction quality simultaneously. To optimize JSCC neural networks using the proposed objective, we derive a differentiable estimation based on variational approximation and the density-ratio trick. Additionally, we design a password-based privacy-protective algorithm that encrypts the private subcodewords, achieving joint optimization with JSCC neural networks. The proposed algorithm involves an encryptor for encrypting private information and a decryptor for recovering it at the legitimate receiver. A loss function is derived based on the maximum entropy principle for jointly training the encryptor, decryptor, and JSCC decoder to maximize eavesdropping uncertainty and improve reconstruction quality. Experimental results show that DPJSCC reduces eavesdropping accuracy on private information by up to 18% and decreases inference time by 10%.