A Comparison of Layering and Stream Replication Video Multicast Schemes

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A Comparison of Layering and Stream Replication
Video Multicast Schemes

• Taehyun Kim and Mostafa H. Ammar
• Networking and Telecommunications Group
• Georgia Institute of Technology
• Atlanta, Georgia

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Research Goal

• A systematic comparison of video multicasting
  schemes designed to deal with heterogeneous
  receivers
• Replicated streams
• Cumulative layering
• Non-cumulative layering

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Stream Replication

• Multiple video streams
• Same content with different data rates
• Receiver subscribes to only one stream
• Example
• DSG (Cheung, Ammar, and Li, 1996)
• SureStream of RealNetworks?
• Intelligent streaming of Microsoft?

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Replicated Stream Multicast
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Cumulative Layering

• 1 base layer enhancement layers
• Base layer
• Independently decoded
• Enhancement layer
• Decoded with lower layers
• Improve the video quality
• Example
• RLM (McCanne, Jacobson, Vetterli, 1996)
• LVMR (Li, Paul, and Ammar, 1998)
• MPEG-2/4, H.263 scalability modes

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Layered Video Multicast
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Layering or Replication?

• Common wisdom states
• Layering is better than replication
• But it depends on
• Layering bandwidth penalty
• Specifics of encoding
• Protocol complexity
• Topological placement of receivers

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Bandwidth Penalty

• Information theoretic results
• R(P, D2) ? R(P, D1, D2)
• Packetization overhead
• Syntactically independent layering
• Picture header
• GOP information
• Macroblock information

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Experimental Comparison
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Comparison by DP
J. Kimura, F. A. Tobagi, J. M. Pulido, P. J.
Emstad, "Perceived quality and bandwidth
characterization of layered MPEG-2 video
encoding", Proc. of the SPIE, Boston, MA, Sept.
1999
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Providing a Fair Comparison

• Need to insure that each scheme is optimized
• Two dimensions
• Selection of stream/layer rates
• Assignments of streams/layers to receivers

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Rate allocation

• Cumulative layering
• Optimal receiver partitioning algorithm (Yang,
  Kim, and Lam)
• Stream replication
• Cumulative rate allocation

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Stream assignment

• Cumulative layering
• Assign as many layers as possible
• Stream replication
• Greedy algorithm

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Comparison Methodology

• Model of network
• Topology
• Available bandwidth
• Placement of source and receivers
• Determine optimal stream rates and allocation
• Evaluate performance

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Performance Metrics

• Average reception rate
• Total bandwidth usage
• Average effective reception rate
• Efficiency

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Network Topology

• GT-ITM
• Number of server 1
• Number of receivers 1,640
• Number of transit domains 10
• Number of layers 8
• Amount of penalty 25

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Data reception rate
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Bandwidth usage
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Effective reception rate
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Efficiency
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Effect of overhead
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Effect of the number of layers
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Clustered Distribution

• Topology consideration
• Layering favors clustered receivers
• Stream replication favors randomly distributed
  receivers
• Simulate when receivers are clustered within one
  transit domain

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Effective reception rate
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Protocol Complexity

• Layered video multicasting
• Multiple join for a receiver
• Large multicast group size
• Replicated stream video multicasting
• One group for a receiver
• Small multicast group size

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Average group size
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Conclusion

• Identified the factors affecting relative merits
  of layering versus replication
• Layering penalty
• Specifics of the encoding
• Topological placement
• Protocol complexity
• Developed stream assignment and rate allocation
  algorithm
• Investigated the conditions under which each
  scheme is superior

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Optimal Quality Adaptation for MPEG-4
Fine-Grained Scalable Video

• Taehyun Kim and Mostafa H. Ammar
• Networking and Telecommunications Group
• Georgia Institute of Technology
• Atlanta, Georgia

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Related Work (1/2)

• S. Nelakuditi, et al, Providing smoother quality
  layered video stream, NOSSDAV 2000
• Goals
• Achieving smoother quality for layered CBR video
  using receiver buffer
• Minimizing quality variation (maximizing runs of
  continuous frames)

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Algorithm

• Forward scan
• Switching between select and discard phase
• Entering select phase if buffer is full
• Entering discard phase if buffer is empty
• Backward scan
• Exploiting the residual buffer
• Extending each run

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Bandwidth Model
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Experimental Result
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Experimental Result
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Related Work (2/2)

• D. Saparilla, et al, Optimal streaming of
  layered video, INFOCOM 2000
• Goal
• Investigating the bandwidth allocation problem to
  minimize loss probability
• Modeling the source video and the available
  bandwidth by stochastic process

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Main Result

• Static policy
• Allocating bandwidth in proportion to long run
  average data rate
• Optimal for infinite length, independent layering
• Threshold-based policy
• If the base layer buffer is below a threshold,
  allocate bandwidth to the base layer

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Research Goal of MPEG4 FGS Quality Adaptation

• Maximization of the perceptual video quality by
  minimizing quality variation
• Accommodation of the mismatch between
• Rate variability of VBR video
• Available bandwidth variability

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MPEG4 FGS Hybrid Scalability

• Base layer
• Enhancement layer
• FGS layer improving video quality
• FGST layer improving temporal resolution

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Rate Variability
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Quality Adaptation Framework
Ck transmission resource
constraint Xk cumulative data size Sk
cumulative selected data size d
threshold
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Optimal Quality Adaptation

• Threshold should be equal to the receiver buffer
  size to achieve
• Minimum quality variability
• Necessary condition of maximum bandwidth
  utilization

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Online Adaptation

• Estimating the threshold point without assuming
  the available bandwidth information in advance
• The available bandwidth is estimated by an MA
  style linear estimator

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Experiment Model
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Bandwidth Variability

• TFRC

• TCP

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Performance over TFRC

• Threshold-based streaming (Infocom00)

• Online adaptation

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Performance over TCP

• Threshold-based streaming

• Online adaptation

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Conclusion

• Accommodated the mismatch between the rate
  variability and the bandwidth variability
• Developed an optimal quality adaptation scheme
  for MPEG4 FGS video to reduce quality variation
• Investigated the perceptual quality of different
  algorithms and options