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

1
Joint Source-Channel Coding to achieve graceful
Degradation of Video over a wireless channel

• By
• Sadaf Ahmed

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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.

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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.

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Source Coding

• Standards like
• H.261/H.263/ H.264 MPEG-1/2/4etc.
• Compression is achieved by exploiting redundancy
• spatial
• temporal

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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.

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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.

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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.

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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.

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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.

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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

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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

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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

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Channel Coding

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

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

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Channels

• Rayleigh Fading Channel
• Rician Fading Channel
• Adittive White Gaussian Noise

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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.

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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.

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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.

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• 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.

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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.

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AWGN

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

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Source coding

• MPEG
• H.26x (future)

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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

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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

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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.

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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.

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Literature Review
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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.

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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.

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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.

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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.

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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.

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Encoder
Decoder
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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.

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• 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.

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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 characteristics9.

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• 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.

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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.

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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.

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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.

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• 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.

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References

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References

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