Joint Source/Channel Coding

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Joint Source/Channel Coding

• Ketan Mayer-Patel

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ALF

• Make the network work within the terms of the
  applications.
• Cant change installed technologies.
• Physical limits and design tradeoffs prevent
  this.
• Optimize the application for the network.
• Requires that the network expose internal
  details.
• Fundamental issue the nature of abstraction.

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Source Coding

• Old problem in signal processing.
• Information source produces a string of symbols.
• Symbols are drawn from a fixed alphabet.
• Symbol distribution is not uniform.
• Problem
• Encode symbols as codewords such that average
  codeword length is minimized.
• AKA Data Compression

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Channel Coding

• Opposite problem of source coding.
• Noisy communication channel.
• A set of source codes to transmit.
• Problem
• Map source codes to channel codes such that
  probability of correct recovery is arbitrarily
  low.
• Shannon shows that if you can characterize the
  channel noise, this is possible.

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Separation Principle

• The really important result.
• Shannons separation principle says that source
  and channel coding can be done independently.

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Consequences

• Seminal work with long lasting consequences.
• Separated source coding and channel coding as
  problems.
• Resulting in two subfields and from there
• Data compression vs. Networking

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Parallel Constructs

• Layering of network stack is a parallel concept
  to the separation principle.
• Why? What do I mean by this?
• Different layers are like different codings.
• Each layer provides a particular service.
• As a whole, arguable provides as good a service
  as if a single protocol for everything.
• Differences?
• Protocols build on top of each other while source
  and channel coding are independent.

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Joint Source/Channel Coding

• Separation principle only works in the limit.
• Need arbitrarily large data set.
• No bound on coding delay.
• JS/CC can improve coding efficiency in more
  realistic, non-limit contexts.
• Separation best in theory.
• JS/CC often best in practice.
• In other words do source coding with channel
  coding in mind and vice versa.
• Sound familiar?

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ALF and JS/CC

• ALFNetworkingJS/CCCommunication
• ALF
• Layers of networking need to be cognizant of each
  other (in particular the very top layer, the
  application).
• JS/CC
• Signal coding needs to be cognizant of
  transmission coding.

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Back to MM Networking

• So how does all of this apply to Multimedia
  Networking?
• What is the stream of source symbols?
• Media data.
• What is the source coding process?
• Compression schemes (MPEG, MP3, etc.)
• What is the channel coding process?
• RTP packetization
• Transport-level protocol

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Putting it together.

• So what should we do to use ALF and JS/CC in this
  context?
• RTP packetization must be done in a media-aware
  manner.
• ALF and JS/CC are not one-way concepts.
• Whats the obvious other direction?
• Media encoding should be done to accommodate
  packet-based communication.
• McCanne thesis (Berkeley, 96) is a great resource
  for learning and thinking about this idea.

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Loss / Quality Tradeoff

• Network-aware encoding is generally about dealing
  with loss.
• Need to characterize loss in target environment
• Packet or bit?
• Bursty or independent?
• Probability?
• Need to characterize distortion induced by loss.
• This can be extremely difficult to do.
• Very non-linear.
• Mayer-Patel, ACM Multimedia 2002

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Dealing with Loss

• Principle 1
• Make transmission data units independently
  useful.
• Whats the difference between TDU and ADU?
• Techniques
• Checkpoint decoder state in each packet.
• What would this mean for MPEG?
• Choose granularity to minimize amount of decoder
  state required.
• Provide bit offsets to avoid non-byte aligned
  shifts.

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Dealing with Loss

• Principle 2
• Limit propogation of error.
• Techniques
• Include redundant low-res impostor.
• Or substitute past information as impostor.
• Break long dependency chains.
• Provide eventual consistency mechanism.
• MPEG example
• Forced I-block in P and B frames.
• Allocate some portion of bandwidth to this
  mechanism
• Static allocation
• Dynamic allocation

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Dealing with Loss

• Principle 3
• Decorrelate successive packets
• Deals with bursty packet loss.
• Techniques
• Interleave TDUs from different ADUs.
• Spread out information from spatially nearby
  areas into separate TDUs.
• Whats the cost of this principle?

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The Art of ALF

• Ideally examine both sides simultaneously.
• A lot like object-oriented design.
• Bad design will hurt.
• General guidelines
• ADU independence.
• Minimum, coordinated state.
• Eventual state coherence.