### 1.1 Related Work

Communication is all about seeking to convey every bit much information as possible over a given channel with as few mistakes as possible. JSCC is a robust and effectual mechanism used for informations communicating over mistake pone and bandwidth limited radio channels. In [ [ I ] ] a brief overview of the most outstanding techniques of JSCC are given. It covers index assignment ( IA ) , unequal mistake protection ( UEP ) , co-optimised vector quantizing and IA ( i.e. channel optimised vector quantizer ) , and direct transition organizing strategies. JSCC methods are developed for image transmittals are presented in [ [ two ] ] [ [ three ] ] [ [ four ] ] [ [ V ] ] . Unlike image transmittal for video transmittal more intelligent and effectual JSCC strategies are necessary due to its demand for higher bandwidth. A JSCC strategy utilised for radio picture transmittals over Code Division Multiple Access ( CDMA ) webs is presented in [ [ six ] ] . In this work, a tight picture spot watercourse is transmitted over multiple-channel of radio CDMA webs. Each picture beginning bed is protected by a merchandise channel codification construction, where row cryptography is a combination of a rate-compatible pierced whirl ( RCPC ) and cyclic redundancy cheque ( CRC ) . Reed-Solomon ( RS ) is applied for column cryptography. This JSCC efficaciously protects familial picture over multipath attenuation channels. Use of the JSCC for picture broadcast medium over a WiMAX web in to heighten the public presentation of Television transmittal over cyberspace protocol ( IP ) is illustrated in [ [ seven ] ] . In [ [ eight ] ] , the JSCC is introduced for scalable picture cryptography ( H.264/SVC ) with low-density parity-check ( LDPC ) channel cryptography. The experimental consequences suggest that the LDPC provides high grade protection to any scalable scene of the SVC. In [ [ nine ] ] writers have proposed a, a intercrossed mistake control technique dwelling of two types of mechanisms: frontward mistake rectification ( FEC ) and retransmission. The aim of this survey is to optimize the tradeoff between overhead spots due to an application of the FEC and the system hold due to an application of the ARQ to retransmit lost packages. JSCC strategy to understate mean deformation if channel conditions are known at a receiver side is proposed in [ [ x ] ] . In this survey JSCC methods are designed for three different scenarios. In the first scenario JSCC strategy evaluates an optimum channel coding rate presuming the beginning coding rate is know. The 2nd scenario finds an optimum beginning coding rate presuming the channel coding rate is know. The last scenario iteratively searches for an optimum spot rate between beginning and channel cryptography. Here a channel-optimised vector quantization ( COVQ ) is applied as beginning cryptography, and the RCPC is used as channel cryptography.

Even though many JSCC methods have been introduced to better the public presentation of 2D picture transmittal, really small work has been proposed for 3D picture. An illustration of the JSCC for 3D picture transmittal application is proposed in [ [ xi ] ] . Unlike 2D picture, two informations watercourses, left and right positions, demands to be considered for beginning cryptography of 3D content. This survey considers the operation between H.264/AVC and rate compatible punctured turbo codifications ( RCPT ) for beginning and channel cryptography severally. To protect tight picture informations from channel mistakes, the construct of unequal mistake protection ( UEP ) is employed to delegate different degrees of protection to each encoded informations divider with respect to their decryption importance. The JSCC method introduced in this chapter aims to better the public presentation of 3D pictures based on the colour-plus-depth 3D representation over the WiMAX based Reyleigh melting channel from a perceptual quality point of position.

### 1.2 Classical Rate-Distortion Theory

Rate deformation theory is a cardinal facet of information theory which provides the theoretical bounds for lossy informations compaction. Theories of rate deformation were created by Claude Elwood Shannon, known as “father of information theory” , in 1948 [ [ xii ] ] , in his initial work on information theory. This addresses the job of finding the minimum sum of information or information ( R ) that should be communicated over a channel, such that the beginning ( input signal ) can be about reconstructed at the receiving system ( end product signal ) without transcending a given deformation ( D ) . The relationship between rate and deformation is illustrated in 5?1. Term rate is normally refers to the figure of spots per informations sample to be transmitted or stored and “distortion” refers to the grade of difference between original and reconstructed signals, normally evaluated by the mean squared mistake ( MSE ) . However, as most lossy compaction techniques operate on informations samples that will be finally perceived by human consumers ( e.g. watching picture and images ) the deformation steps should be intelligent to pattern on human perceptual experience. To day of the month, since the human perceptual experience theoretical accounts are less good developed for image and picture, lossy compaction techniques still rely on simple statistical step such as MSE, although with less correlativity with respects to HVS.

Ideal noiseless transmittal channels should bring forth the same massages or symbols, X, emitted by the beginning at the finish. However transmittal damages, such as noise, in the channel alter the emitted symbols, ensuing in a different symbol infinite Y at the receiving system. See a simple theoretical account of an mistake pone communicational channel shown in xxx. Let ‘s presume XXXX illustrates the forward passages of the channel. Here X and Y represent the alphabets of symbols transmitted and received severally, during a unit clip over the channel. Let, P ( yj|xi ) defines the conditional chance distribution maps of end product symbols yj for a given input eleven, where nineteen and yj Y.

If the channel is intended to present yj when eleven transmitted, so the mistake chances are defined by P ( yj|xi ) for all J ? I. For the channel, common information, I ( xi ; yj ) , measures the sum of information that symbols xi and yj convey about each other. I ( xi ; yj ) , is defined as follows:

### ( 5.1 )

In pattern, most transmittal channels are see to lie between perfect transportation ( i.e. each yj unambiguously identifies a peculiar eleven ) and zero transportation ( i.e. yj is wholly unrelated to xi ) Average common information is defined to analyze the general instance.

### ( 5.2 )

H ( X ) denotes the information of the end product signal X and H ( X|Y ) denotes the conditional information of the input signal ( X ) given the end product signal ( Y ) . Equation 5.2 provinces that the mean information conveyed per symbol equals the beginning information minus conditional information.

The solution for rate and deformation job can be achieved by understating the rate-distortion map given below:

### ( 5.3 )

Where R is the information rate and D is an mean deformation. I ( X ; Y ) which describes the mean common information between an original beginning ( Ten: where the beginning selects symbols from an alphabet Ten ) and a reconstructed information ( Y ) , . Equation 5.1 says that for a given maximal mean deformation Dmax, the rate deformation map R ( D ) defines the lower edge for the transmittal bit-rate. The minimisation is over all conditional chance distributions P ( yj|xi ) for which the joint distribution P ( yj ; xi ) satisfies the expected deformation restraint. The set of defines all the conditional distributions of P ( yj|xi )

Conditional chance P ( y | x ) is considered as an built-in and fixed belongings of the communicational channel defined by the features of the noise in the channel. The joint chance distribution of X and Y is wholly determined by the nature of the channel and the distribution of messages, degree Fahrenheit ( x ) , to be transmitted over the channel. Under these restraints, the aim is to maximise the rate of information communication over the noisy channel. The appropriate step for this is known as the common information, The theoretical upper edge of common information is know as the channel capacity and is given by:

C = max_ { degree Fahrenheit } I ( X ; Y ) . !

Channel capacity has the subsequent belongings related to conveying information at rate R, where Roentgen is by and large spots per message or symbol. For a communicating system where the information rate R is & lt ; C and coding mistake ? is & gt ; 0, it is ever possible to convey with an randomly little mistake, such that the maximum chance of mistake is less than an acceptable degree ? . In add-on, for any rate R & gt ; C, it is unattainable to convey with randomly little block mistake. The aim of channel cryptography is to happen about optimum codifications that can be used to convey informations over an mistake pone channels with an acceptable mistake at a rate stopping point to impart capacity. However, in most practical picture communicating systems, the quality of familial picture varies due to fluctuations in the allowable bandwidth restrictions. Therefore, the maximal perceptual quality, under the rate restraint, can be achieved by the work outing the followers:

### ( 5.4 )

The set of ? is defines the solution infinite of conditional distributions P ( yj|xi ) for which the joint distribution P ( yj ; xi ) satisfies the expected rate restraint.

### 1.3 Joint Source and Channel Coding for 3D Video

In this subdivision, the frame work of the proposed JSCC to better the public presentation of colour-plus-depth 3D picture transmittal over wireless channels is discussed. The difference between the JSCC for 2D picture and the JSCC for 3D picture is that the traditional 2D picture has merely one beginning constituent while the 3D picture consists of two beginning constituents: coloring material picture and deepness map.

The overall system theoretical account considered for the proposed JSCC for colour-and-depth representation of 3D picture illustrated in 5?3. At the sender ( Tx ) , colour and deepness pictures are individually compressed by H.264/AVC beginning cryptography and so protected by low-density parity-check ( LDPC ) codifications. The end product spot watercourses are rearranged to acquire individual end product at the multiplexer. Subsequently, the end product from multiplexer is transmitted by WiMAX over a Rayleigh attenuation channel. At the receiving system ( Rx ) , received informations watercourse is separated back to 2 informations watercourses before decoded by LDPC and H.264/AVC decipherers, severally. At the terminal of the procedure, coloring material and deepness map are reconstructed.

The chief construct of JSCC is that both the beginning cryptography and channel cryptography are adapted harmonizing to impart conditions in order to minimise the deformation. Distortion in video communicating can be separated into two major types. The first type is the quantisation deformation introduced by lossy beginning encryption and the 2nd type is caused by channel noise. These deformations are merely called “source distortion” and “channel distortion” . The overall deformation is equal to the add-on of beginning and channel deformations. A popular step for deformation is a average square mistake ( MSE ) . The overall image deformation at the receiver terminal can be defined as the MSE between the standard picture frame and the original 1. But it is a good known fact that due to the deficiency of correlativities with the human ocular system ( HVS ) MSE can non measure the quality as by a panel of human [ 13 ] . The chief aim of this chapter is to look into on minimising the consequence of these two types of deformations, utilizing a JSCC attack, from a perceptual quality point of position.

### H.264/AVC Source Coding

Since early 1990s, when the picture coding engineering was in its immatureness, international criterions, consecutive, H.261 [ [ xiii ] ] , MPEG-1 [ [ xiv ] ] , MPEG-2/H.262 [ [ xv ] ] , H.263 [ [ xvi ] ] , and MPEG-4 ( Part 2 ) [ [ xvii ] ] have been the motive behind the success of digital picture compaction. In 2003 JVT ( Joint video Team ) developed H.264/AVC beginning coding criterion and today it is considered as one of the most powerful video compaction criterions of all clip. This can accomplish about twice the coding addition when compared to former picture compaction criterions like H.263. Surveies have shown that [ [ xviii ] ] H.264/AVC can non merely offer high quality services for high-bandwidth webs but besides an acceptable quality service for low-bandwidth services. H.264/AVC criterion is capable of supplying proficient solutions to a wide fury of application countries that covers all assortments of digital tight picture, including picture broadcast medium, picture on demand ( VOD ) , Multimedia Messaging Services ( MMS ) and consecutive or synergistic storage on magnetic and optical devices. Furthermore, due to flexible and customizable deign of the codec new applications may be deployed over the existing architecture. This is because the design architecture covers a Video Coding Layer ( VCL ) , which is intended to expeditiously stand for the picture content, and a Network Abstraction Layer ( NAL ) , which organize the VCL representation of the picture in a mode to let the same picture sentence structure to be compatible in different web environments. These characteristics, along with several others, assistance H.264/AVC to execute well better than any anterior picture coding standard under a broad scope of fortunes and application environments.

### LDPC channel cryptography

LDPC codifications were originally invented by Gallager in early 1960 ‘s [ [ xix ] ] as an mistake rectifying codification. After the innovation LPDC codifications were mostly disregarded and was rediscovered by Mackay in 1999 [ [ xx ] ] . Since so they have experienced a singular return in the last few old ages. As a consequence of the important development of the LDPC codifications, it found an application as an optional channel coding technique to be used in the WiMAX criterion [ [ xxi ] ] . LDPC codifications are considered as capacity-approaching codifications, which mean that the codifications allow transmittal at rates near to the theoretical upper limit, as defined by the Shannon bound, for a symmetric memory-less channels over a really big codification length. For case, the public presentation of the LDPC codification is merely 0.0045 dubniums below the theoretical upper limit, for a codification length of one million spots.

LDPC codifications in WiMAX is based on a set of ( one or more ) cardinal coding rates: 1/2, 2/3A, 2/3B, 3/4A, 3/4B, and 5/6. Each LDPC codification in the criterion is defined by a para cheque matrix H of size m ? N, where m refers to the figure of para spots and n refers to the length of end product package. The figure of systematic spots ( information spots ) of the codification is thousand = n – m. 2/3A, 2/3B and 3/4A, 3/4B has the same cryptography rates but different parity-check matrices H. Parity look into matrix H is obtained by spread outing the generator base matrix by replacing the entries with a square matrix ( z ? omega ) , where the matrix dimension is equal to the block size. The base matrices defined in the IEEE 802.16e criterion [ 69 ] for the six cardinal codification rates are given in Appendix B.

### Mobile WiMAX ( Worldwide Interoperability for Microwave Access )

Mobile WiMAX is an emerging telecommunications engineering for which provides nomadic wireless cyberspace entree. The high information rate and Quality of Service ( QoS ) provided by WiMAX engineering make it attractive to multimedia applications, such as picture telephone, picture gambling, and picture broadcast medium. IEEE 802.16e-2005 criterion [ 75 ] [ [ xxii ] ] defines the formal specificities of Mobile WiMAX. Mobile WiMAX criterion was developed by the WiMAX forum and is an amendment to IEEE 802.16d-2004, ( Fixed WiMAX criterion ) to present support for mobility. The engineering promises high informations rates, up to 70 Mbps, and broad coverage, coverage radius of up to 50 kilometer, at a lower cost. Consequently, Mobile WiMAX has gain the most commercial attending to day of the month and is being successfully deployed in many states. To carry through high efficiency, throughput and dependability, several techniques are built into the MAC and Physical beds of the nomadic WiMAX criterion. In add-on, security and quality of service ( QoS ) mechanisms are besides incorporated. Packet construction of nomadic WiMAX is good suited for non line of sight communicating, which is the typical Mobile WiMAX user experience. Fading distribution of non line of sight communicating is closely correlated to Rayleigh distribution, because it processes the statistical clip changing belongingss of non line of sight communicating nexus [ [ xxiii ] ] [ [ xxiv ] ] [ [ xxv ] ] . Channel coding for mistake rectification is necessary for dependable communicating due to impart noise, attenuation and other transmittal damages. For this intent the IEEE 802.16e-2005 criterion suggests the usage of following coding methods [ [ xxvi ] ] :

* Low-density para cheque ( LDPC ) – allows six cardinal codification rates: ? , 2/3A, 2/3B, 3/4A, 3/4B and 5/6

* Convolution Turbo Code ( CTC ) – allows cardinal codification rate of 1/3 and extra codification rates utilizing puncturing: 2/3, ? and 5/6

* Convolutional Code ( CC ) – allows a female parent codification rate of ? and extra rates utilizing puncturing: 2/3, ? and 5/6

In most instances LDPC codifications is preferred for channel cryptography in nomadic WiMAX. Advantages of LDPC over CTC and CC codifications are discussed in [ 80 ] .

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