Joint Source-Channel Coding to achieve graceful Degradation of Video over a wireless channel

By

Sadaf Ahmed

Abstract

The demand for multimedia transmission is increasing with every passing day. The need for compression arises due to high data rates in case of multimedia compared to text etc. The compressed stream, more susceptible to channel errors, is channel coded to mitigate the effects on the quality. The increase in the use of wireless networks makes it even harder for high quality video transmission. As wireless channels are low bandwidth and time varying in nature, unlike their wired counterparts, the quality degrades drastically. The impact of various characteristics of wireless channels on video is analyzed, to better understand the need for schemes that help in degrading gracefully over a wireless channel. In order to reduce the degradation in quality joint source and channel coding is required. Error concealment techniques also help in achieving graceful degradation.

Source Coding

The compression or coding of a signal (e.g., speech, text, image, video) has been a topic of great interest for a number of years.

Source compression is the enabling technology behind the multimedia revolution we are experiencing.

The two primary applications for data compressing are storage and transmission.

Source Coding

Standards likeH.261/H.263/ H.264 MPEG-1/2/4etc.

Compression is achieved by exploiting redundancyspatial temporal

Video Transmission

Due to very high data rates compared to other data types, video transmission is very demanding.

The channel bandwidth and the time varying nature of the channel impose constraints to video transmission.

Video Transmission System

In a video communication system, the video is first compressed and then segmented into fixed or variable length packets and multiplexed with other types of data, such as audio.

Unless a dedicated link that can provide a guaranteed quality of service (QoS) is available between the source and the destination, data bits or packets may be lost or corrupted, due to either traffic congestion or bit errors due to impairments of the physical channels.

Video Transmission system

The video encoder has two main objectives: to compress the original video sequence and to make the encoded sequence resilient to errors.

Compression reduces the number of bits used to represent the video sequence by exploiting both temporal and spatial redundancy.

To minimize the effects of losses on the decoded video quality, the sequence must be encoded in an error resilient way.

Video Transmission System

For many source-channel coding applications, the exact details of the network infrastructure may not be available to the sender.

The sender can estimate certain network characteristics, such as the probability of packet loss, the transmission rate and the round-trip-time (RTT).

In most communication systems, some form of CSI is available at the sender, such as an estimate of the fading level in a wireless channel or the congestion over a route in the Internet.

Such information may be fed back from the receiver and can be used to aid in the efficient allocation of resources.

Video Transmission System

On the receiver side, the transport and application layers are responsible for de-packetizing the received transport packets, channel decoding, and forwarding the intact and recovered video packets to the video

decoder. The video decoder typically employs error detection and

concealment techniques to mitigate the effects of packet loss.

The commonality among all error concealment strategies is that they exploit correlations in the received video sequence to conceal lost information.

Channel Models

The development of mathematical models which accurately capture the properties of a transmission channel is a very challenging but extremely important problem.

For video applications, two fundamental properties of the communication channel are the probability of packet loss and the delay needed for each packet to reach the destination.

In wireless networks, besides packet loss and packet truncation, bit error is another common source of error.

Packet loss and truncation are usually due to network traffic and clock drift, while bit corruption is due to the noisy air channel

Wireless Channels

Compared to wired links, wireless channels are much noisier because of fading, multi-path, and shadowing effects,

which results in a much higher bit error rate (BER) and consequently an even lower throughput.

Smaller Bandwidth

Channel coding

Improves the small scale link performance by adding redundant data bits in the transmitted message so that if an instantaneous fade occurs in the channel, the data may still be recovered at the receiver.

Block codes, Convolutional Codes and turbo codes

Channel Coding

Two basic techniques used for video transmission are FEC and Automatic Repeat reQuest (ARQ)

…

Effect of various channel conditions on some particular source coded video data.

Channels

Rayleigh Fading Channel Rician Fading Channel Adittive White Gaussian Noise

Rayleigh Fading Distribution

Is commonly used to describe the statistical time varying nature of the received envelope of a flat fading signal, or the envelope of an individual multipath component.

Rayleigh Fading Channel

first-order Markov channel models can be used to adequately predict the behavior of a mobile “Rayleigh” fading channel and hence improve the reliability of bidirectional mobile communications systems.

Rician Fading Distribution

When there is a dominant stationary (non fading) signal component present, such as line of sight propagation path, the small scale fading envelope is Rician.

Random multipath components arriving at different angles are superimposed on a stationary dominant signal.

At the output of an envelope detector, this has an effect of adding a dc component to the random multipath.

…

As the dominant signal becomes weaker, the composite signal resembles a noise signal which has an envelope that is Rayleigh.

Thus the Rician distribution degenerates to a Rayleigh distribution when the dominant component fades away.

AWGN

In communications, the additive white Gaussian noise (AWGN) channel model is one in which the only impairment is the linear addition of wideband or white noise with a constant spectral density (expressed as watts per hertz of bandwidth) and a Gaussian Distribution of amplitude. The model does not account for the phenomena of fading, frequency selectivity, interference, nonlinearity or dispersion. However, it produces simple, tractable mathematical models which are useful for gaining insight into the underlying behavior of a system before these other phenomena are considered.

AWGN

Mobile radio channel impairments cause the signal at the receiver to distort or fade significantly as compared to AWGN channels.

Source coding

MPEG H.26x (future)

Why Joint?

Source coding reduces the bits by removing redundancy

Channel coding increase the bits by adding redundant bits

To optimize the two Joint source-channel coding

Joint Source-Channel Coding

JSCC usually faces three tasks: finding an optimal bit allocation between

source coding and channel coding for given channel loss characteristics;

designing the source coding to achieve the target source rate;

and designing the channel coding to achieve the required robustness

Techniques

Rate allocation to source and channel coding and power allocation to modulated symbols

Design of channel codes to capitalize on specific source characteristics

Decoding based on residual source redundancy Basic modification of the source encoder and

decoder structures given channel knowledge.

Conclusion

The Video over a time varying wireless channel undergoes certain degradation in quality. This effect on quality varies with the characteristics of a particular channel. In order to estimate the effects of a channel on a video sequence, an analysis based on the simulation results is required. The effects on the quality, a major requirement in multimedia transmission, enforce the need for efficient source and channel coding. The source coding reduces the size of multimedia stream by exploiting redundancy while the channel coding adds redundancy to decrease the effects of a channel on multimedia quality. This emphasizes the need for joint source and channel coding schemes.

Literature Review

A survey on the techniques for the transport of MPEG-4 video over wireless networks

In order to improve the quality of the reconstructed video transmitted over such noisy channels, several error resilient techniques have been developed. A survey of various techniques (error resilient and scalable video coding) for the transmission of MPEG-4 video over a wireless channel is given in [1].

Transport of Wireless Video using separate, concatenated and Joint Source Channel Coding

In [2], various joint source-channel coding schemes are surveyed and how to use them for compression and transmission of video over time varying wireless channels is discussed.

A video transmission system based on human visual model In [3] a joint source and channel coding scheme

is proposed which takes into account the human visual system for compression. To improve the subjective quality of compressed video a perceptual distortion model (Just Noticeable Distortion) is applied. In order to remove the spatial and temporal redundancy 3D wavelet transform is used. Under bad channel conditions errors are concealed by employment of a slicing and joint source channel coding method is used.

Adaptive code rate decision of joint source-channel coding for wireless video

[4] proposes a joint source channel coding method for wireless video based on adaptive code rate decision.

Since error characteristics vary with time by several channel conditions, e.g. interference and multipath fading in wireless channels, an FEC scheme with adaptive code rate would be more efficient in channel utilisation and in decoded picture quality than that with fixed code rate. Allocating optimal code rate to source and channel codings while minimising end-to-end overall distortion is a key issue of joint source-channel coding

The transmitter side of the video transmission system under consideration for joint source-channel coding consists of video encoder, channel encoder, and rate controller which estimates channel characteristics and decides the code rate to allocate the total channel rate to source and channel encoders.

Adaptive joint source-channel coding using rate shaping

An adaptive joint source channel coding is proposed in [5] which use rate shaping on pre-coded video data. Before transmission, portions of video stream are dropped in order to satisfy the network bandwidth requirements. Due to high error rates of the wireless channels channel coding is also employed. Along with the source bit stream, the channel coded segments go through rate shaping depending on the network conditions.

Encoder

Decoder

Adaptive Segmentation based joint source-channel coding for wireless video transmission [6] proposes a joint source-channel coding

scheme for wireless video transmission based on adaptive segmentation. For a given standard, the image frames are adaptively segmented into regions in terms of rate distortion characteristics and bit allocation is performed accordingly.

A fully integrated joint source-channel coding scheme instead of a sequential approach is given in [7].

[8] proposes channel coding scheme for strongly varying channels.

Channel Adaptive Resource Allocation for Scalable Video Transmission over 3G Wireless Network Based on the minimum distortion,

resource allocation between source and channel coders is done, taking into consideration the time varying wireless channel condition and scalable video codec characteristics[9].

An end-to-end distortion-minimized resource allocation scheme using channel-adaptive hybrid UEP and delay-constrained ARQ error control schemes proposed in [8]. Specifically, available resources are periodically allocated between source, UEP and ARQ. Combining the estimation of available channel condition with the media characteristic, this distortion-minimized resource allocation scheme for scalable video delivery can adapt to the varying channel/network condition and achieve minimal distortion.

Joint Source Coding and transmission power management for energy efficient wireless video communications

in [10], an analytical model based on the notion of outage capacity is used. In this model, a packet is lost whenever the fading realization results in the channel having a capacity less than the transmission rate.

Analysis of the Markov Character of a General Rayleigh Fading Channel

In [11], an analysis which takes into consideration both the phase information and the amplitude to get the analytical expressions which can be used to find if a non stationary object is necessarily first order Markov, is given. The behavior of the Rayleigh Fading channel can be predicted using a first order Markov according to it.

Optimal Packet Scheduling for Wireless Video Streaming with Error Prone Feedback

An optimal transmission strategy which jointly takes into account the source and channel conditions using a partially observable Markov decision process is proposed in [12]. The proposed method has resulted in reducing the high variance in the Peak Signal to Noise Ratio (PSNR) values found previously.

There are several attributes which characterize a wireless channel [13], which are random so a statistical representation is associated with each one of them. These features include Path loss which describes the loss in power as the radio signal,

propagates in space The most widely used path loss models are the Hata Model, the Okumura Model.

Doppler spread is a measure of the spectral broadening caused by the time rate of change of the mobile channel and is defined as the range of frequencies over which the received Doppler spread is essentially non-zero.

Delay Co-Channel Interference Fading characteristics, which accounts for the combined effect of

multiple propagation paths, rapid movements of mobile units (transmitters/receivers) and reflectors.

References

1. Bo Yan, Ng, K.W, “A survey on the techniques for the transport of MPEG-4 video over wireless networks”, IEEE Transactions on Consumer Electronics, Volume 48, Issue 4, Nov. 2002, pp 863-873.

2. Robert E. Van Dyck and David J. Miller, “Transport of Wireless Video using separate, concatenated and Joint Source Channel Coding”, proceedings of the IEEE, October 1999, pp. 1734-1750.

3. Yimin Jiang, Junfeng Gu and John S. Baras, “A video transmission system based on human visual model”, IEEE 1999, pp. 868-873.

4. Jae Cheol Kwon and Jae-Kyoon Kim, “Adaptive code rate decision of joint source-channel coding for wireless video”, IEEE Electronic Letters, 5th December 2002, vol 38, pp. 1752-1754.

5. Trista Pei-chun Chen and Tsuhan Chen, “Adaptive joint source-channel coding using rate shaping”, IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, volume 2, 2002, pp. 1985-1988.

6. Yingiun Su, Jianhua Lu, Jing Wang, Letaief K.B. and Jun Gu, “Adaptive Segmentation based joint source-channel coding for wireless video transmission” Vehicular Technology Conference, volume 3, 6-9 May 2001, pp. 2076-2080.

7. Fan Zhai, Yiftach Eisenberg, Thrasyvoulos N. Pappas, Randall Berry and Sggelos K. Katsaggelos, “An integrated joint source-channel coding framework for video transmission over packet lossy network”, International Conference on Image Processing 2004, Volume 4, 24-27 Oct 2004, pp. 2531-2534.

References

8. J. Hagenaeuer, T. Stockhammer, C. Weiss and A. Donner, “Progressive source coding combined with regressive channel coding for varying channels”, 3rd ITG Conference Source and Channel Coding, Jan. 2000, pp. 123-130.

9. Qian Zhang, Wenwu Zhu and Ya-Qin Zhang, “Channel Adaptive Resource Allocation for Scalable Video Transmission over 3G Wireless Network”, IEEE Transactions on Circuits and Systems for video Technology”, Volume 14, 8August 2004, pp. 1049-1063.

10. Y. Eisenberg, C. E. Luna, T. N. Pappas, R. Berry and A. K. Katsaggelos, “Joint Source Coding and transmission power management for energy efficient wireless video communications”, IEEE Transactions on Circuits and Systems for Video Technology, Volume 12, Issue 6, June 2002, pp 411-424.

11. Roger Dalke and George Hufford, “Analysis of the Markov Character of a General Rayleigh Fading Channel”, NTIA Technical Memorandum, April 2005.

12. Dihong Tian, Xiaohuan Li, Ghassan Al-Regib, Yucel Altunbasak and Joel R. Jackson, “Optimal Packet Scheduling for Wireless Video Streaming with Error Prone Feedback”

13. Theodore S. Rappaport, “Wireless Communications Principles and Practices”, Second Edition.