In this paper, nonlinear distributed joint source-channel coding (JSCC) schemes for transmission of multivariate Gaussian sources over a Gaussian multiple access channel are proposed and analyzed. The main contribution is a zero-delay JSCC named Distributed Quantizer Linear Coder (DQLC), which performs relatively close the information theoretical bounds, improves when the correlation among the sources increases, and does not level off as the signal-to-noise ratio (SNR) becomes large. Therefore it outperforms any linear solution for sufficiently large SNR. Further an extension of DQLC to an arbitrary code length named Vector Quantizer Linear Coder (VQLC) is analyzed. The VQLC closes in on the performance upper bound as the code length increases and can potentially achieve the bound for any number of independent sources. The VQLC leaves a gap to the bound whenever the sources are correlated, however. JSCC achieving the bound for arbitrary correlation has been found for the bivariate case, but that solution is significantly outperformed by the DQLC/VQLC when there is a low delay constraint. This indicates that different approaches are needed to perform close to the bounds when the code length is high and low. The VQLC/DQLC also apply for bandwidth compression of a multivariate Gaussian transmitted on point-to-point links.

In this paper we generalize an existing distributed zero-delay joint source-channel coding scheme for communication of a multivariate Gaussian on a Gaussian Multiple Access Channel named Distributed Quantization Linear Coder (DQLC) to arbitrary code length. Although the DQLC is well performing, it leaves a certain gap to the performance upper bound (or distortion lower bound) based on arbitrary code length. The purpose of this paper is to determine if the generalization of the DQLC to arbitrary code length, named Vector Quantization Linear Coder (VQLC), can close the gap to the bound when the code length is large. Our results show that the VQLC mapping has the potential to reach the upper bound for any number of Gaussian sources at high SNR when the sources are uncorrelated. We also approximately determine the VQLC performance as a function of code length for the special case of two sources.

In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and a linear continuous mapping. The proposed schemes show promising performance which improves with increasing correlation and are robust against variations in noise level. Both schemes also exhibit a constant gap to the performance upper bound when the channel signal-to-noise ratio gets large.

We study the communication problem in a sensor network which consists of multiple sensor nodes that observe memoryless Gaussian sources which are inter-correlated. The observations are transmitted over orthogonal additive white Gaussian noise channels, and all source symbols are to be recovered at the receiver. We focus on communication schemes which utilize direct source to channel mappings that operate on a symbol-by-symbol basis to ensure zero coding delay. The distortion lower bound for the network with more than two sensors case is derived. Optimal linear schemes, both distributed and cooperative, are presented. Results show that the gap to the performance upper bound is large when there is high correlation and it increases significantly when the network size is large. We then present nonlinear mappings which can be implemented distributedly and show that they can provide substantial gain when the correlation is close to one. Examples are given for networks with two and three nodes.

The problem of lossy source channel communication under a received power constraint in a simple Gaussian sensor network is studied in this paper. A group of sensors are placed to observe a common Gaussian source. The noisy observations are then transmitted over a Gaussian multiple access channel (MAC) to the sink, where the source is estimated with a quadratic distortion criterion using all received sensor observations. We propose an analogue transmission scheme that uses noiseless causal feedback from the sink to remove correlation between the observation samples, combined with time division multiple access of the MAC. The proposed scheme offers same performance with reduced received power and sensor network size, compared with optimal transmission scheme with single channel use; and converges to the absolute performance bound with low received power level for the same use of bandwidth.

We studied the problem of distributed coding and transmission of inter-correlated sources with memory. Different from the conventional distributed source coding structure which relies on design of effective channel codes to model the inter-correlation and quantizer, the proposed system utilizes distributed compressed sensing [1] for signal dimension reduction through linear matrix operations and dimension expansion for protection against channel noise through a hybrid scalar quantizer linear coder [2]. The proposed system is optimized for minimum end-to-end distortion under a transmission energy constraint. Its performance is verified through simulation and can serve as a good starting point for designing similar analogue based dimension reduction- expansion schemes for applications in sensor networks.

As a newly released industrial communication standard, WirelessHART complements the ever so successful HART field devices by providing the possible means for communicating via wireless channels. The WirelessHART standard is designed to offer simple configuration, flexible installation and easy access of instrument data, and at the same time, ensure robust and reliable communications. In this paper, we first look closely into the specifications and present a comprehensive overview of the standard by summarizing the main functions of the various protocol layers. We then survey the literature and identify amongst the existing methods and algorithms, which ones can be effectively adopted in implementing the standard. More specifically, we set our focus on issues relating to realization of the medium access layer and the network manager, which are essential in creating a successful WirelessHART network for specific applications.

In the recent years, the peer-to-peer (P2P) overlay network has been a promising architecture for multimedia streaming services besides its common use for efficient file sharing. By simply increasing the number of peers, the P2P overlay network can meet the high bit rate requirements of multimedia applications. Optimal peer selection for newly joining peers is one of the important problems, especially in wireless networks which have limited resources and capacity, since the peer selection process has a direct impact on the throughput of the underlay network and the co-existing unicast traffic. In this paper we tackle the problem of peer selection for streaming applications over wireless ad hoc networks. We devise a novel peer selection algorithm which maximizes the throughput of the underlay network, and at the same time makes P2P streaming friendly towards the co-existing data traffic. The proposed receiver based rate allocation and peer selection (RPS) algorithm is derived using the network utility maximization (NUM) framework. The algorithm solves the peer selection and rate allocation problem distributedly while optimally adapting the medium access control (MAC) layer parameters and is easily extensible to large P2P networks. Simulation results show that by using the proper price exchange mechanism, the peer receivers can effectively maximize the throughput of the underlay network by intelligently selecting its source peers.

In our recent work, an analytic power-channe/error-rate-distortion (PERD) model was developed to estimate the end-to-end distortion of power constrained portable devices in real-time video communications over bandwidth limited and lossy channels. A random mode selection scheme is implemented after obtaining optimal percentages of intra, inter and skip mode through a constrained optimization process. In this paper, advanced solution to the problem of mode selection for each macroblock (MB) is proposed. A concealment aware PERD model is first derived, which takes error concealment at the decoder into consideration to estimate the end-to-end distortion. By using this model, optimal percentages of intra, inter and skip modes that minimize decoder distortion are obtained under any power and rate constraints. An intelligent mode selection scheme is then generalized by ranking the priority of MB according to how well it gets reconstructed if lost. Experimental results show that, the new framework consistently achieves better power-rate-distortion (P-R-D) performance with little extra computation load, which is critical in real-time communications of portable devices.

The potential problem of oversubscribing receivers in receiver-driven multicast is addressed. We present a framework based on harmonizing the erasure-resilience properties of video with existing congestion control algorithms. The result offers subscription alternatives for receivers in which a penalty in terms of visual quality will be experienced by oversubscribing receivers. Thus, we provide an incentive for performing proper congestion control. The presented framework is independent of specific congestion control algorithms. Simulation results show the intended performance.

An encoder based rate smoothing scheme which uses explicit slice-based mode selection has been proposed. The algorithm provides significantly smoother bitstream from a variable bit rate (VBR) encoder and reduces the network queuing delay while maintaining quality for the end-user. In this paper we investigate the error robustness of the proposed scheme based on H.264/AVC codec. The results show that, the proposed scheme maintains error resilience properties without extra delay. Compared to the standard frame based scheme, the algorithm provides almost the same average distortion while reducing the distortion variance which helps improving the subjective video quality. We investigate the performance in random and bursty packet loss environments, and it shows that the proposed scheme is applicable for error prone networks, for example, wireless networks.

Rate smoothing is essential for achieving lower delay when transmitting real-time video over the network. Recently, "explicit slice-based mode selection" (ESM) is proposed as a new way of achieving this goal together with its inherent quality smoothness and error resilience features. However previous studies focus on the practical aspects and do not address an optimized solution. In this paper, we propose a rate-distortion (RD) optimized solution for finding the best location and size of the intra-coded slices. The experimental results show that for a target bit rate the optimized scheme is able to offer performance close to that of mode selection on a macroblock level, over wireless channels with different packet loss rates. Moreover, the optimized ESM algorithm provides significant advantages of granular bit stream prioritization for network transmission. However, the RD based optimization is in general computationally expensive. We therefore propose a heuristic approach which incorporates both channel statistics and sequence characteristics. Results show that it yields close to optimal performance at lower complexity.

A joint source channel coding scheme for error resilient image transmission is proposed. A practical image coder was introduced in AN Kim et al, (2004) using modified differential pulse coded modulation (DPCM) codec with multi-rate processing and adaptive entropy coding. In this paper the residual redundancy of the prediction error image is exploited by using turbo codes for both data compression and error protection. In the paper we deal with robust transmission of the source over a BSC channel, but the results can be easily extended for non binary channels. Note also that simple modification of the quantizer allows for progressive transmission and successive refinement of information. With properly chosen rate and puncturing, the system is able to approach the limit theoretically attainable and to outperform the separated approach that consists on the concatenation of the system in AN Kim et al, (2004) and the best turbo codes for the same spectral efficiency.

The modified differential pulse coded modulation (DPCM) codec with multi-rate processing has been shown to able to efficiently code the source with monotonically decreasing spectrum at low bit rates [A.N. Kim and T.A. Ramstad]. A practical image coder is designed based on this approach. Two dimensional DPCM is used along with decimation and interpolation to reduce the number of transmitted samples. The decimation rate depends on the signal spectrum and the bit rate. Further bit rate reduction is achieved through adaptive entropy coding. Wiener filter is appended in the decoder for minimizing distortion caused by quantization noise. The decimation filter can be implemented using simple IIR filters. The necessary side information is low. Simulation results show that the coder is able to give good compression performance at low bit rates which is superior to conventional DPCM codec and JPEG. Subjective quality can be as good as JPEG2000. While at very low bit rates the proposed codec is able to retain certain image characteristics better than JPEG2000.