Presently, both wireless communications and multimedia communications have experienced unequaled rapid growth and commercial success. Building on advances of network infrastructure, low-power integrated circuits, and powerful signal processing/compression algorithms, wireless multimedia services to support digital video applications such as videophone, video conferencing, video streaming, video on demand (VoD), and video surveillance, likely finds widespread acceptance. Many wireless multimedia applications require video coding schemes and underlying transport that can provide acceptable quality of service to the end users. Due to the burst errors nature of wireless channels and the error propagation property of compressed video, suitable source and channel coding schemes are required to handle such conditions. This chapter provides overview of video compression techniques, and the latest video coding standard, H.264/AVC, its implication for the wireless video transmission, and our research contribution on joint source-channel coding for wireless video transmission.
Multimedia is defined as media that utilizes a combination of different content forms (http://en.wikipedia.org/wiki/Multimedia). Those forms are regarded as text, audio, still images, animation, video, and interactivity. To exchange those data among humans or among humans and machines, the field “multimedia communication” is established. Multimedia communication refers to the representation, storage, retrieval, distribution of machine-processable information expressed in multiple media. It can be divided according to the type of media such as data, text, audio, image and video communications. Some example applications are data transfer and fax. For audio communication example applications are telephony and sound broadcasting. For video communication example applications are video conferencing, television, and high-definition television.
The elements of multimedia communication system are divided into person-to-person and person-to-machine communication. Person-to-person communications are systems where humans are the end users of the system connected through telecommunication networks. This kind of system enables interactive and real-time multimedia data to flow between parties. Examples are MSN messaging, Skype, and video conferencing system. Another kind of system is a person-to-machine communications where humans interact with computer servers such as music server, video server, or a simple text information server. The computer servers perform the task of processing, storage, and retrieval of multimedia data. The example scenario is when users browse through the web page and decide to stream the video clips to their computer terminals. To develop such a system, careful considerations should be taken into account; those include users and network requirements and specification of multimedia terminals. From the user’s point of view, the capabilities of dynamically controlling the multimedia information in terms of connection interaction, quality on demand, and friendly user interfaces are important as well as the ability to prepare and present different multimedia information according to time budget constraints and available terminal resources. Standardization of multimedia representation is also a necessary component in multimedia services. The key requirements of new multimedia services would involve instant availability, real-time information transfer, and universal access from any terminal. The network technology needs to be continuously developed to support high speed, varying bit rates and the ability to synchronize different types of information. In the end, there are still many issues that need to be addressed in developing multimedia terminals: basic techniques for compression and coding various multimedia data, basic techniques for organizing, storing, and retrieving multimedia signals, basic technique for accessing, searching and browsing individual multimedia documents.
In the above mentioned digital multimedia content, speech, audio, and video are the most widely researched in terms of applications areas such as internet telephony, streaming video and audio, digital audio and video broadcasting, etc. Nevertheless, the bit rate required to make such applications possible ranges from the order of kilobits for speech to gigabits for video. Thus to realize these, a lot of research and standardization effort have been aimed at the efficient transmission of these digital multimedia content. The signal processing community has devoted a lot of effort in developing efficient multimedia source coding techniques. The communication community has also devoted efforts in terms of channel coding techniques and efficient method of multimedia transmission and distribution. Nevertheless, joint efforts from research communities are presented today. From all the forms of digital multimedia content, video plays an increasingly important role as the emerging multimedia communication and distribution system. Regardless of its extremely large size that makes it more difficult to find effective schemes to compress and transmit flawlessly through IP and wireless medium.
In this chapter, we discuss the video compression techniques, and the latest video coding standard, H.264/AVC, its implication for the wireless video transmission, and our research contribution on joint source-channel coding for wireless video transmission.
Key Terms in this Chapter
ARQ: Automatic Repeat Request, a system of transmission protocols for error control.
PSNR: Peak Signal to Noise Ratio, a measure of signal fidelity by computing the ratio the peak signal power and the corrupting noise power.
MPEG-4 Part 10: The same as H.264.
LCR: Level Crossing Rate, the rate (or frequency) that a fading signal crosses a specified threshold in the positive going direction.
FEC: Forward Error Correction, system of error control schemes for data transmission which adds redundant information in order to provide robustness to channel errors.
H.264/AVC: ITU-T: Standard for advanced video coding, also known as MPEG-4 Part 10.
ADF: Average Duration of Fade, the average amount of time that a fading signal stays below a specified threshold.
BER: Bit Error Rate, the percentage of bits that have errors relative to the total number of bits received during transmission.
Complete Chapter List
Shiguo Lian, Yan Zhang
Shiguo Lian, Yan Zhang
Pramod A. Jamkhedkar, Gregory L. Heileman
Deepali Brahmbhatt, Mark Stamp
Mercè Serra Joan, Bert Greevenbosch, Anja Becker, Harald Fuchs
Hugo Jonker, Sjouke Mauw
Pallavi Priyadarshini, Mark Stamp
L. Badia, A. Erta, U. Malesci
Ramya Venkataramu, Mark Stamp
Nicolas Anciaux, Luc Bouganim, Philippe Pucheral
Guojun Wang, Yirong Wu, Geyong Min, Ronghua Shi
Supavadee Aramvith, Rhandley D. Cajote
M. Hassan Shirali-Shahreza, Mohammad Shirali-Shahreza
Pradeep K. Atrey, Abdulmotaleb El Saddik, Mohan Kankanhalli
Esther Palomar, Juan M.E. Tapiador, Julio C. Hernandez-Castro, Arturo Ribagorda
Andreas U. Schmidt, Nicolai Kuntze
Goo-Rak Kwon, Sung-Jea Ko
Frank Y. Shih, Yi-Ta Wu
Guangjie Liu, Shiguo Lian, Yuewei Dai, Zhiquan Wang
Minglei Liu, Ce Zhu
Hsuan T. Chang, Chih-Chung Hsu