Radio spectrum has become a scarce and priced resource due to the rapid growth of wireless networks. However, recent surveys conducted by the FCC indicate that a large part of the allotted frequency spectrum lies unused. Cognitive radio systems, built on the software defined radios, allow the efficient usage of these unused frequency spectrum. Cognitive radio systems can be modeled as a multiple access channel in which certain users have the priority (primary users) while others (cognitive or secondary users) are allowed to access the channels without causing any interference to the primary users. However a secondary user’s transmissions not only encounter high levels of uncertainty and variability in the number of channels available to them, but they also suffer data losses if a primary user activity occurs. Under such rigid constraints, the reliable transmission of real time multimedia of a secondary user with an acceptable quality of service becomes challenging. Multimedia transmission in a cognitive system requires channel adaptive source and channel coding schemes. In order to address this problem, this thesis investigates and develops a novel joint source-channel coding (JSCC) approach. The proposed JSCC allows the dynamic generation of codes, which minimizes the end-to-end distortion. This JSCC is based on quantized frame expansions to introduce redundancy into transmitted data. An algorithm has been developed to determine the optimal trade-off between redundancy and quantization rate, under a constraint on channel capacity. The proposed approach does not require the communication of any overhead data between the transmitter and receiver. When compared to codes commonly used to deal with packet losses, simulation results indicate that the proposed JSCC can achieve lower distortion for secondary user’s transmissions in cognitive radio systems.

<p>This thesis presents two practical coding schemes based on low-density generatormatrix (LDGM) codes, for two cases of the multiple description (MD) problem. The first one is for the two description problem for finite-alphabet sources with Hammingdistortion measure. The proposed MD code targets the Zhang-Berger region, which is the best inner bound known so far for the corresponding MD rate-distortion region.The coding scheme can be regarded as a practical implementation of a theoreticalsequential coding system for the corner points of a related rate-region, where the random codebooks are replaced by multilevel LDGM codebooks and the encoding at each stage is performed via a message passing algorithm. This coding system is further applied in three notable cases: 1) no excess sum-rate case for binary sources;2) successive refinement for general finite-alphabet sources, 3) no excess marginal ratefor uniform binary sources. Furthermore, in order to assist the code design in the noexcesssum-rate case for binary sources, the exact expression of the distortion regionand of the auxiliary variables needed to achieve its boundary, are derived, which is another important contribution of the thesis.</p> <p>The second proposed MD code is for the case of L descriptions with individual and central distortion constraints, for the memoryless Gaussian source with squared distortion measure. It is shown first that the coding problem for an arbitrary point on the dominant face of an L-description El Gamal-Cover (EGC) rate region, can be converted to that for a vertex of a K-description EGC rate region for some K<=2L-1,where the latter problem can be solved via successive coding. The practical coding scheme reduces each successive coding step to a Gaussian quantization operation, and implements this operation using multilevel LDGM codes.</p> <p>The LDGM-based coding schemes are extensively tested in practice for all aforementionedcases. The experimental results show very good performance, verifyingthat the proposed schemes can approach the theoretical rate-distortion bounds.</p> / Doctor of Philosophy (PhD)

No description available.

The aim of source coding is to represent information as accurately as possible using as few bits as possible and in order to do so redundancy from the source needs to be removed. The aim of channel coding is in some sense the contrary, namely to introduce redundancy that can be exploited to protect the information when being transmitted over a nonideal channel. Combining these two techniques leads to the area of joint source-channel coding which in general makes it possible to achieve a better performance when designing a communication system than in the case when source and channel codes are designed separately. In this thesis four particular areas in joint source-channel coding are studied: analog (i.e. continuous) bandwidth expansion, distributed source coding over noisy channels, multiple description coding (MDC) and soft decoding. A general analog bandwidth expansion code based on orthogonal polynomials is proposed and analyzed. The code has a performance comparable with other existing schemes. However, the code is more general in the sense that it is implementable for a larger number of source distributions. The problem of distributed source coding over noisy channels is studied. Two schemes are proposed and analyzed for this problem which both work on a sample by sample basis. The first code is based on scalar quantization optimized for a certain channel characteristics. The second code is nonlinear and analog. Two new MDC schemes are proposed and investigated. The first is based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. In case that some of the transmitted descriptors are lost during transmission this side information (if received) can be used to estimate the lost descriptors based on the received ones. The second scheme uses permutation codes to produce different descriptions of a block of source data. These descriptions can be used jointly to estimate the original source data. Finally, also the MDC method multiple description coding using pairwise correlating transforms as introduced by Wang et al. is studied. A modi fication of the quantization in this method is proposed which yields a performance gain. A well known result in joint source-channel coding is that the performance of a communication system can be improved by using soft decoding of the channel output at the cost of a higher decoding complexity. An alternative to this is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this thesis we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. The issue on how to best construct finite-bandwidth representations of soft information is also studied. / QC 20100920

Advancements in video coding, compact media display, and communication devices, particularly in emerging broadband wireless access networks, have created many foreseeable and exciting applications of video broadcast/multicast over the wireless meidum. For efficient and robust wireless video broadcast/multicast under fading, this thesis presents and examines a novel cross-layer framework that exploits the interplay between applying protections on a successively refinable video source and transmitting through a layered broadcast/multicast channel. The framework is realistically achieved and evaluated by using multiple description coding (MDC) on a scalable video source and using superposition coding (SPC) for layered broadcast/multicast transmissions. An analytical model using the total received/recovered video bitstreams from each coded wireless broadcast/multicast signal is developed, which serves as a metric of video quality for the system analysis and optimization. An efficient methodology has demonstrated that optimal power allocations and modulation selections can be practically determined to improve the broadcast/multicast video quality. From the information-theoretical perspective, a general closed-form formula is derived for the end-to-end distortion analysis of the proposed framework, which is applicable to any (n, k) protection code applied on a successive refinable source with a Gaussian distribution over layered Gaussian broadcast channels. The results reveal the scenarios for the proposed framework to lead to a lower distortion than a legacy system without any protection. By analyzing the characteristics of the closed-form formula, an efficient O(n log n) algorithm is developed to determine optimal k values in the (n, k) protection codes that minimize the distortion under the framework. Finally, a cross-layer design of logical SPC modulation is introduced to achieve layered broadcast/multicast for scalable video. It serves as an alternative for practically implementing the proposed framework of coded wireless video broadcast/multicast, if the hardware-based SPC component is not available in a wireless system. In summary, the thesis presents comprehensive analyses, simulations, and experiments to understand, investigate, and justify the effectiveness of the proposed cross-layer framework of coded wireless video broadcast/multicast. More importantly, this thesis contributes to the advancement in the related fields of communication engineering and information theory by introducing a new design dimension in terms of protection. This is unique when compared to previously-reported layered approaches that are often manipulating conventional parameters alone such as power and modulation scheme. The impact of this dimension was unapparent in the past, but is now proven as an effective means to enable high-quality, efficient, and robust wireless video broadcast/multicast for promising media applications.

Advancements in video coding, compact media display, and communication devices, particularly in emerging broadband wireless access networks, have created many foreseeable and exciting applications of video broadcast/multicast over the wireless meidum. For efficient and robust wireless video broadcast/multicast under fading, this thesis presents and examines a novel cross-layer framework that exploits the interplay between applying protections on a successively refinable video source and transmitting through a layered broadcast/multicast channel. The framework is realistically achieved and evaluated by using multiple description coding (MDC) on a scalable video source and using superposition coding (SPC) for layered broadcast/multicast transmissions. An analytical model using the total received/recovered video bitstreams from each coded wireless broadcast/multicast signal is developed, which serves as a metric of video quality for the system analysis and optimization. An efficient methodology has demonstrated that optimal power allocations and modulation selections can be practically determined to improve the broadcast/multicast video quality. From the information-theoretical perspective, a general closed-form formula is derived for the end-to-end distortion analysis of the proposed framework, which is applicable to any (n, k) protection code applied on a successive refinable source with a Gaussian distribution over layered Gaussian broadcast channels. The results reveal the scenarios for the proposed framework to lead to a lower distortion than a legacy system without any protection. By analyzing the characteristics of the closed-form formula, an efficient O(n log n) algorithm is developed to determine optimal k values in the (n, k) protection codes that minimize the distortion under the framework. Finally, a cross-layer design of logical SPC modulation is introduced to achieve layered broadcast/multicast for scalable video. It serves as an alternative for practically implementing the proposed framework of coded wireless video broadcast/multicast, if the hardware-based SPC component is not available in a wireless system. In summary, the thesis presents comprehensive analyses, simulations, and experiments to understand, investigate, and justify the effectiveness of the proposed cross-layer framework of coded wireless video broadcast/multicast. More importantly, this thesis contributes to the advancement in the related fields of communication engineering and information theory by introducing a new design dimension in terms of protection. This is unique when compared to previously-reported layered approaches that are often manipulating conventional parameters alone such as power and modulation scheme. The impact of this dimension was unapparent in the past, but is now proven as an effective means to enable high-quality, efficient, and robust wireless video broadcast/multicast for promising media applications.

In this thesis, robust transmission of 3D models represented by static or time consistent animated meshes is studied from the aspects of scalable coding, multiple description coding (MDC) and error resilient coding. First, three methods for MDC of static meshes are proposed which are based on multiple description scalar quantization, partitioning wavelet trees and optimal protection of scalable bitstream by forward error correction (FEC) respectively. For each method, optimizations and tools to decrease complexity are presented. The FEC based MDC method is also extended as a method for packet loss resilient transmission followed by in-depth analysis of performance comparison with state of the art techniques, which pointed significant improvement. Next, three methods for MDC of animated meshes are proposed which are based on layer duplication and partitioning of the set of vertices of a scalable coded animated mesh by spatial or temporal subsampling where each set is encoded separately to generate independently decodable bitstreams. The proposed MDC methods can achieve varying redundancy allocations by including a number of encoded spatial or temporal layers from the other description. The algorithms are evaluated with redundancy-rate-distortion curves and per-frame reconstruction analysis. Then for layered predictive compression of animated meshes, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial/temporal prediction and angular relations of triangles between current and previous frames. The experimental results show that compared to state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level. Finally, optimal quality scalability support is proposed for the state of the art scalable predictive animated mesh coding structure, which only supports resolution scalability. Two methods based on arranging the bitplane order with respect to encoding or decoding order are proposed together with a novel trellis based optimization framework. Possible simplifications are provided to achieve tradeoff between compression performance and complexity. Experimental results show that the optimization framework achieves quality scalability with significantly better compression performance than state of the art without optimization.

<p>The aim of source coding is to represent information as accurately as possible using as few bits as possible and in order to do so redundancy from the source needs to be removed. The aim of channel coding is in some sense the contrary, namely to introduce redundancy that can be exploited to protect the information when being transmitted over a nonideal channel. Combining these two techniques leads to the area of joint source–channel coding which in general makes it possible to achieve a better performance when designing a communication system than in the case when source and channel codes are designed separately. In this thesis two particular areas in joint source–channel coding are studied: multiple description coding (MDC) and soft decoding. Two new MDC schemes are proposed and investigated. The first is based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. In case that some of the transmitted descriptors are lost during transmission this side information (if received) can be used to estimate the lost descriptors based on the received ones. The second scheme uses permutation codes to produce different descriptions of a block of source data. These descriptions can be used jointly to estimate the original source data. Finally, also the MDC method multiple description coding using pairwise correlating transforms as introduced by Wang et al is studied. A modification of the quantization in this method is proposed which yields a performance gain. A well known result in joint source–channel coding is that the performance of a communication system can be improved by using soft decoding of the channel output at the cost of a higher decoding complexity. An alternative to this is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this thesis we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. The issue on how to best construct finite-bandwidth representations of soft information is also studied.</p>

The aim of source coding is to represent information as accurately as possible using as few bits as possible and in order to do so redundancy from the source needs to be removed. The aim of channel coding is in some sense the contrary, namely to introduce redundancy that can be exploited to protect the information when being transmitted over a nonideal channel. Combining these two techniques leads to the area of joint source–channel coding which in general makes it possible to achieve a better performance when designing a communication system than in the case when source and channel codes are designed separately. In this thesis two particular areas in joint source–channel coding are studied: multiple description coding (MDC) and soft decoding. Two new MDC schemes are proposed and investigated. The first is based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. In case that some of the transmitted descriptors are lost during transmission this side information (if received) can be used to estimate the lost descriptors based on the received ones. The second scheme uses permutation codes to produce different descriptions of a block of source data. These descriptions can be used jointly to estimate the original source data. Finally, also the MDC method multiple description coding using pairwise correlating transforms as introduced by Wang et al is studied. A modification of the quantization in this method is proposed which yields a performance gain. A well known result in joint source–channel coding is that the performance of a communication system can be improved by using soft decoding of the channel output at the cost of a higher decoding complexity. An alternative to this is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this thesis we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. The issue on how to best construct finite-bandwidth representations of soft information is also studied. / QC 20101124

<p>This thesis treats the problem of source coding in situations where the encoded data is subject to errors. The typical scenario is a communication system, where source data such as speech or images should be transmitted from one point to another. A problem is that most communication systems introduce some sort of error in the transmission. A wireless communication link is prone to introduce individual bit errors, while in a packet based network, such as the Internet, packet losses are the main source of error.</p><p>The traditional approach to this problem is to add error correcting codes on top of the encoded source data, or to employ some scheme for retransmission of lost or corrupted data. The source coding problem is then treated under the assumption that all data that is transmitted from the source encoder reaches the source decoder on the receiving end without any errors. This thesis takes another approach to the problem and treats source and channel coding jointly under the assumption that there is some knowledge about the channel that will be used for transmission. Such joint source--channel coding schemes have potential benefits over the traditional separated approach. More specifically, joint source--channel coding can typically achieve better performance using shorter codes than the separated approach. This is useful in scenarios with constraints on the delay of the system.</p><p>Two different flavors of joint source--channel coding are treated in this thesis; multiple description coding and channel optimized vector quantization. Channel optimized vector quantization is a technique to directly incorporate knowledge about the channel into the source coder. This thesis contributes to the field by using channel optimized vector quantization in a couple of new scenarios. Multiple description coding is the concept of encoding a source using several different descriptions in order to provide robustness in systems with losses in the transmission. One contribution of this thesis is an improvement to an existing multiple description coding scheme and another contribution is to put multiple description coding in the context of channel optimized vector quantization. The thesis also presents a simple image coder which is used to evaluate some of the results on channel optimized vector quantization.</p>