Wireless Video Communications: Second to Third Generation and Beyond / Edition 1

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Overview

Bridging the gap between the video compression and communication communities, this unique volume provides an all-encompassing treatment of wireless video communications, compression, channel coding, and wireless transmission as a joint subject. WIRELESS VIDEO COMMUNICATIONS begins with relatively simple compression and information theoretical principles, continues through state-of-the-art and future concepts, and concludes with implementation-ready system solutions.

This book's deductive presentation and broad scope make it essential for anyone interested in wireless communications. It systematically converts the lessons of Shannon's information theory into design principles applicable to practical wireless systems. It provides in a comprehensive manner "implementation-ready" overall system design and performance studies, giving cognizance to the contradictory design requirements of video quality, bit rate, delay, complexity error resilience, and other related system design aspects.

Topics covered include

  • information theoretical foundations
  • block-based and convolutional channel coding
  • very-low-bit-rate video codecs and multimode videophone transceivers
  • high-resolution video coding using both proprietary and standard schemes
  • CDMA/OFDM systems, third-generation and beyond adaptive video systems.

WIRELESS VIDEO COMMUNICATIONS is a valuable reference for postgraduate researchers, system engineers, industrialists, managers and visual communications practitioners.

Bridging the gap between the video compression and communication communities, this unique volume provides an all-encompassing treatment of wireless video communications, compression, channel coding, and wireless transmission as a joint subject.

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Product Details

Meet the Author

Lajos Hanzo has enjoyed a prolific 24-year career during which he has held various research and academic positions in Hungary, Germany, and the United Kingdom. He has coauthored five books on mobile radio communications and published over 300 research papers on a variety of topics. Dr. Hanzo’s research interests cover the entire spectrum of mobile multimedia communications, including voice, audio, video and graphic source compression, channel coding, modulation, networking and the joint optimization of these system components. He holds a chair in communications in the Department of Electronics and Computer Science at the University of Southampton, England, and he is a consultant to Multiple Access Communications Ltd.

Peter J. Cherriman graduated in 1994 with an M.Eng. In information engineering from the University of Southampton. Since 1994, he has been with the Department of Electronics and Computer Science at the University of Southampton, where he completed his Ph.D. in mobile video networking. Dr. Cherriman is working on projects for the Mobile Virtual Centre of Excellence, U.K. His current areas of research include robust video coding, microcellular radio systems, power control, dynamic channel allocation, and multiple access protocols.

Jurgen Streit received his Diploma in electronic engineering from the Aachen University of Technology, Germany, in 1993. Since 1992 he has been with the Department of Electronics and Computer Science at the University of Southampton, working with the Mobile Multimedia Communications Research Group. Dr. Streit earned a Ph.D. in image coding, and he is currently working as a software consultant.

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Read an Excerpt

Chapter 1: Information Theory

1.1 Issues in Information Theory

The ultimate aim of telecommunications is to communicate information between two geographically separated locations via a communications channel with adequate quality. The theoretical foundations of information theory accrue from Shannon's pioneering work [13-16], and hence most tutorial interpretations of his work over the past fifty years rely fundamentally on [13-16]. This chapter is no exception in this respect. Throughout this chapter we make frequent references to Shannon's seminal papers and to the work of various authors offering further insights into Shannonian information theory. Since this monograph aims to provide an all-encompassing coverage of video compression and communications, we begin by addressing the underlying theoretical principles using a light-hearted approach, often relying on worked examples.

Early forms of human telecommunications were based on smoke, drum or light signals, bonfires, semaphores, and the like. Practical information sources can be classified as analog and digital. The output of an analog source is a continuous function of time, such as, for example, the air pressure variation at the membrane of a microphone due to someone talking. The roots of Nyquist's sampling theorem are based on his observation of the maximum achievable telegraph transmission rate over bandlimited channels [17]. In order to be able to satisfy Nyquist's sampling theorem the analogue source signal has to be bandlimited before sampling. The analog source signal has to be transformed into a digital representation with the aid of timetime andlitude-discretization using sampling and quantization.

The output of a digital source is one of a finite set of ordered, discrete symbols often referred to as an alphabet. Digital sources are usually described by a range of characteristics, such as the source alphabet, the symbol rate, the symbol probabilities, anal the probabilistic interdependence of symbols. For example, the probability of u following q in the English language is p = 1, as in the word "equation." Similarly, the joint probability of all pairs of consecutive symbols can be evaluated.

In recent years, electronic telecommunications have become prevalent, although most information sources provide information in other forms. For electronic telecommunications, the source information must be converted to electronic signals by a transducer. For example, a microphone converts the air pressure waveform p(t) into voltage variation v(t), where

v(t) = c * p(t - r), (1.1.)

and the constant c represents a scaling factor, while r is a delay parameter. Similarly, a video camera scans the natural three-dimensional scene using optics and converts it into electronic waveforms for transmission.

The electronic signal is then transmitted over the communications channel and converted back to the required form, which may be carried out, for example, by a loudspeaker. It is important to ensure that the channel conveys the transmitted signal with adequate quality to the receiver in order to enable information recovery. Communications channels can be classified according to their ability to support analog or digital transmission of the source signals in a simplex, duplex, or half-duple fashion over fixed or mobile physical channels constituted by pairs of wires, Time Division Multiple Access (TDMA) time-slots, or a Frequency Division Multiple Access (FDMA) frequency slot.

The channel impairments may include superimposed, unwanted random signals, such as thermal noise, crosstalk via multiplex systems from other users, man-made interference from car ignition, fluorescent lighting, and other natural sources such as lightning. Just as the natural sound pressure wave between two conversing persons will be impaired by the acoustic background noise at a busy railway station, similarly the reception quality of electronic signals will be affected by the above unwanted electronic signals. In contrast, distortion manifests itself differently from additive noise sources, since no impairment is explicitly added. Distortion is more akin to the phenomenon of reverberating loudspeaker announcements in a large, vacant hall, where no noise sources are present.

Some of the channel impairments can be mitigated or counteracted; others cannot. For example, the effects of unpredictable additive random noise cannot be removed or "subtracted" at the receiver. Its effects can be mitigated by increasing the transmitted signal's power, but the transmitted power cannot be increased without penalties, since the system's nonlinear distortion rapidly becomes dominant at higher signal levels. This process is similar to the phenomenon of increasing the music volume in a car parked near a busy road to a level where the amplifier's distortion becomes annoyingly dominant.

In practical systems, the Signal-to-Noise Ratio (SNR) quantifying the wanted and unwanted signal powers at the channel's output is a prime channel parameter. Other important channel parameters are its amplitude and phase response, determining its usable bandwidth (B), over which the signal can be transmitted without excessive distortion. Among the most frequently used statistical noise properties are the probability density function (PDF), cumulative density function (CDF), and power spectral density (PSD).

The fundamental communications system design considerations are whether a high-fidelity (HIFI) or just acceptable video or speech quality is required from a system, which predetermines, among other factors, its cost, bandwidth requirements, as well as the number of channels available, and has implementational complexity ramifications. Equally important are the issues of robustness against channel impairments, system delay, and so on. The required transmission range and worldwide roaming capabilities, the maximum available transmission speed in terms of symbols/sec, information confidentiality, reception reliability, convenient lightweight, solar-charged design, are similarly salient characteristics of a communications system.

Information theory deals with a variety of problems associated with the performance limits of the information transmission system, such as that depicted in Figure 1.1. The components of this system constitute the subject of this monograph; hence they will be treated in greater depth later in this volume. Suffice it to say at this stage that the transmitter seen in Figure 1.1 incorporates a source encoder, a channel encoder, an interleaver, and a modulator and their inverse functions at the receiver. The ideal source encoder endeavors to remove as much redundancy as possible from the information source signal without affecting its source representation fidelity (i.e., distortionlessly), and it remains oblivious of such practical constraints as a finite delay and limited signal processing complexity. In contrast, a practical source encoder will have to retain a limited signal processing complexity and delay while attempting to reduce the source representation bit rate to as low a value as possible. This operation seeks to achieve better transmission efficiency, which can be expressed in terms of bit-rate economy or bandwidth conservation.

The channel encoder re-inserts redundancy or parity information but in a controlled manner in order to allow error correction at the receiver. Since this component is designed to ensure the best exploitation of the re-inserted redundancy, it is expected to minimize the error probability over the most common channel, namely, the so-called Additive White Gaussian Noise (AWGN) channel, which is characterized by a memoryless, random distribution of channel errors. However, over wireless channels, which have recently become prevalent, the errors tend to occur in bursts due to the presence of deep received signal fades induced by the distructively superimposed multipath phenomena. This is why our schematic of Figure 1.1 contains an interleaver block, which is included in order to randomize the bursty channel errors. Finally, the modulator is designed to ensure the most bandwidth-efficient transmission of the source- and channel encoded, interleaved information stream, while maintaining the lowest possible bit error probability. The receiver simply carries out the corresponding inverse functions of the transmitter. Observe in the figure that besides the direct interconnection of the adjacent system components there are a number of additional links in the schematic, which will require further study before their role can be highlighted. Thus, at the end of this chapter we will return to this figure and guide the reader through its further intricate details.

Some fundamental problems transpiring from the schematic of Figure 1.1, which were addressed in depth by a range of references due to Shannon [13-16], Nyquist [17], Hartley [18], Abramson [19], Carlson [20], Raemer [21], and Ferenczy [22] and others are as follows:

  • What is the true information- generation rate of our information sources? If we know the answer, the efficiency of coding and transmission schemes can be evaluated by comparing the actual transmission rate used with the source's information emission rate. The actual transmission rate used in practice is typically much higher than the average information delivered by the source, and the closer these rates are, the better is the coding efficiency.
  • Given a noisy communications channel, what is the maximum reliable information transmission rate? The thermal noise induced by the random motion of electrons is present in all electronic devices, and if its power is high, it can seriously affect the quality of signal transmission, allowing information transmission only at low-rates.
  • Is the information emission rate the only important characteristic of a source, or are other message features, such as the probability of occurrence of a message and the joint probability of occurrence for various messages, also important?
  • In a wider context, the topic of this whole monograph is related to the blocks of Figure 1.1 and to their interactions, but in this chapter we lay the theoretical foundations of source and channel coding as well as transmission issues and define the characteristics of an ideal Shannonian communications scheme.

Although numerous excellent treatises are available on these topics, which treat the same subjects with a different flavor [20, 22, 23], our approach is similar to that of the above classic sources; since the roots are in Shannon's work, references [13-16, 24, 25] are the most pertinent and authoritative sources.

In this chapter we consider mainly discrete sources, in which each source message is associated with a certain probability of occurrence, which might or might not be dependent on previous source messages. Let us now give a rudimentary introduction to the characteristics of the AWGN channel, which is the predominant channel model in information theory due to its simplicity. The analytically less tractable wireless channels of Chapter 2 will be modeled mainly by simulations in this monograph, although in Chapter 10 some analytical results are also provided in the context of Code Division Multiple Access (CDMA) systems.

1.2 Additive White Gaussian Noise Channel

1.2.1 Background

In this section, we consider the communications channel, which exists between the transmitter and the receiver, as shown in Figure 1.1. Accurate characterization of this channel is essential if we are to remove the impairments imposed by the channel using signal processing at the receiver. Here we initially consider only fixed communications links whereby both terminals are stationary, although mobile radio communications channels, which change significantly with time, are becoming more prevalent.

We define fixed communications channels to be those between a fixed transmitter and a fixed receiver. These channels are exemplified by twisted pairs, cables, wave guides, optical fiber and point-to-point microwave radio channels. Whatever the nature of the channel, its output signal differs from the input signal. The difference might be deterministic or random, but it is typically unknown to the receiver. Examples of channel impairments are dispersion, nonlinear distortions; delay, and random noise.

Fixed communications channels can often be modeled by a linear transfer function, which describes the channel dispersion. The ubiquitous additive Gaussian noise (AWGN) is a fundamental limiting factor in communications via linear time-invariant (LTI) channels. Although the channel characteristics might change due to factors such as aging, temperature changes, and channel switching, these variations will not lie apparent over the course of a typical communication session. It is this inherent time invariance that characterizes fixed channels.

An ideal, distortion-free communications channel would have a flat frequency response and linear phase response over the frequency range of -oo .... + oo, although in practice it is sufficient to satisfy this condition over the bandwidth (B) of the signals to be transmitted, as seen in Figure 1.2. In this figure, A(w) represents the magnitude of the channel response at frequency w, and ø(w) = wT represents the phase shift at frequency w due to the circuit delay T.

Practical channels always have some linear distortions due to their bandlimited, nonflat frequency response and nonlinear phase response. In addition, the group-delay response of the channel, which is the derivative of the phase response, is often given....

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Table of Contents

Preface.

Acknowledgments.

Contributors.

I. Transmission Issues.

1. Information Theory.

2. The Propagation Environment.

3. Convolutional Channel Coding.

4. Block-Based Channel Coding.

5. Modulation and Transmission Techniques.

6. Video Traffic Modeling and Multiple Access.

7. Co-Channel Interference.

8. Channel Allocation.

9. Second-Generation Mobile Systems.

10. CDMA Systems: Third-Generation and Beyond.

II. Video Systems Based on Proprietary Video Codecs.

11. Fractal Image Codecs.

12. Very Low Bit-Rate DCT Codes.

13. VQ Codecs and Multimode Video Transceivers.

14. Low Bit-Rate Parametric Quad-Tree-Based Codecs and Multimode Videophone Transceivers.

III. High-Resolution Image Coding.

15. Low-Complexity Techniques.

16. High-Resolution DCT Coding.

IV. Video Systems Based on Standard Video Codecs.

17. An ARQ-Assisted H.261-Based Reconfigurable Multilevel Videophone System.

18. Comparison of the H.261 and H.263 Codecs.

19. A H.263 Videophone System for Use over Mobile Channels.

20. Error Rate Based Power Control.

21. Adaptive Single-Carrier, Multicarrier, and CDMA-based Video Systems.

Glossary.

Bibliography.

Subject Index.

Author Index.

About the Authors.

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