Proxy-Assisted Techniques for Delivering Continuous Multimedia Streams

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Proxy-Assisted Techniques for Delivering
Continuous Multimedia Streams

• Lixin Gao, Zhi-Li Zhang, and Don Towsley

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Agenda

• Related work
• Proxy-Assisted Video Delivery Architecture
• Proxy-Assisted Catching
• Proxy-Assisted Selective Catching
• Simulation results
• Conclusion

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Related Work

• Server-push
• -gt Typically designed for hot (frequently
  requested) objects
• -gt Fixed number of multicast channels

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Limitations of current technology

• Server and network resources (Server I/O
  bandwidth and network bandwidth) are major
  limiting factors in widespread usage of video
  streaming over the internet
• Need techniques to efficiently utilize server and
  network resources
• Service latency and popularity of video object
  should be considered

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Proxy-Assisted Video Delivery Architecture
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Advantages of proxy-assisted video delivery

• Latency reduction without increasing demand on
  backbone network resources
• Need to store only the initial frames hence
  feasible with large data volume
• I/O bandwidth requirement on proxy server is
  insignificant, since responsible for limited
  number of clients

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Classification

• Proxy-assisted catching Suited for hot video
  objects
• Proxy-assisted selective catching Even suited
  for cold (less frequently requested) video
  objects

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Advantages of proposed architectures

• Reduce the resources requirements at central
  server
• Reduce service latency experienced by clients

Assumptions

• Client can receive data from 2 channels
  simultaneously

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Proxy-Assisted Catching

• Reduces service latency by allowing clients to
  join an ongoing broadcast
• Clients catch-up by retrieving initial frames
  using unicast channel from proxy

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Proxy-Assisted Catching
Partition function used
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Optimizing

• Server and network bandwidth are major
  bottleneck. Hence reducing total number of
  channels required
• Trade-off between
• -gt Number of dedicated channels by server
• -gt Storage space required by proxy

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Terms involved

• N No. of video objects on central server
• L Length of video
• ? Request rate (Poisson distribution)
• K Server channels to broadcast video
• K Optimal number of server channels
• i Video object no.
• j Broadcasting frame

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Calculation

• No. of proxy channels required
• Total no. of channels required
• Tradeoff between number of server channels and
  expected number of proxy channels required for
  catch-up

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Calculation contd..

• Optimization problem
• Expected number of channels

Optimal no. of server channels
Optimal no. of proxy channels
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Controlled Multicast

• Client pull technique
• Allows client to join the ongoing multicast if it
  requests with a certain threshold time Ti
• Else a new multicast channel is allocated

Proxy-assisted Controlled Multicast

• Proxy pre-store the initial Ti frames of video
• Missing portion of video is send separately
  through a unicast channel
• Good technique for cold video objects

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Comparison with Proxy-Assisted Controlled
Multicast

• Total no. of channels required for controlled
  multicast is
• For large value of ? no. of channels required by
  proxy-assisted catching is less
• Verified using following setup
• L 90 min. video object

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Observation
0.4
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Proxy-Assisted Selective Catching

• Combines Proxy-Assisted Catching and Controlled
  Multicast
• Broadcast most frequent videos using
  Proxy-Assisted Catching and less frequent videos
  using Controlled Multicast

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Classifying Hot and Cold videos

• Hot video if

Total no. of channels required using catching
Total no. of channels required using controlled
multicast
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Simulation results

• Simulation settings
• N No. of video objects on central server
• ? Request rate (Poisson's distribution)
• Simulates 150 hours of client requests
• Ki Broadcasting channels for hot video
  objects
• Remaining channels for controlled multicast
• First-come-first-serve basis

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Assumptions

• Sufficient proxy resources to store prefixes for
  all videos
• Proxy server has 40GB of storage space and I/O
  bandwidth of 88 Mb/s

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Waiting time vs. total number of channels
? 50
710
900
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Waiting time vs. Arrival rate

• ? varies from 40 to 80
• Total no. of channels 700

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Total no. of channels vs. arrival rate
100
150
Performance of selective catching and catching
same
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Waiting time vs. Server channels
700
460

• 36 saving in number of channels required at
  central server

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Number of channels vs. Arrival rate

• Significant reduction in central server channel
  requirement

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Waiting time vs. Server channels

• Advantage of proxy-assisted selective catching
  does not critically depend on availability of
  proxy storage space

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Conclusion

• Approach is proved using quite realistic
  simulations without any major assumptions
• If the arrival rate exceeds beyond certain
  assumptions then the service latency will
  increase

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