RANI NALAMARU

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RANI NALAMARU DEPARTMENT OF COMPUTER
SCIENCE BALL STATE UNIVERSITY
Efficient Transmission of Stored Video for
Improved Management of Network Bandwidth
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Overview of presentation

• Introduction
• Background
• Problem Statement
• The New VP Algorithm
• Evaluation of OBA, Optimal and VP Algorithms
• Summary and Future work

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Introduction

• Network video
• Many emerging applications
• Entertainment, Distance learning, Catalogue
  browsing etc.

Storage
Network
Video packet
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Introduction

• Networking challenges for video
• Huge bandwidth requirement if no compression
• With compression traffic is bursty
• Bursty traffic complicates network management
• Goal Efficient transmission of high quality
  stored streaming video

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Introduction

• Video compression and burstiness
• Burstiness can occur due to
• Type of frames used in encoding
• Background changes or changes in scene content

Frame sizes of a stored video
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Background

• Transmission plan
• Pre-calculated schedule to transmit a video file
• Mechanism to smooth the bandwidth requirement

Network
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Background
Work-ahead smoothing

• Have all video frames in advance
• Knowledge of frame sizes

• Given the parameters
• Frame sizes for n frames
• Client buffer size b

• Constraints at the client buffer
• Avoid buffer underflow
• Avoid buffer overflow

Goal Find a transmission plans with minimum
number of
rate changes and minimized sum of rate variation
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Background

• Optimal Bandwidth Allocation (OBA) algorithm
  (1995)
• Developed by Feng, Jahanian, and Sechrest (Univ.
  of Michigan)
• Goal of OBA algorithm is to develop a
  transmission plan with
• smallest peak bandwidth
• largest minimum bandwidth
• fewest possible changes in bandwidth (rate
  changes)

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Background

• Optimal algorithm (1996)
• Developed by Salehi, Kurose, and Towsley (Univ.
  of Mass.)
• Goal of Optimal algorithm was to develop a
  transmission plan with
• smallest peak bandwidth
• least variation between bandwidth changes (rate
  variation)

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Problem Statement

• Problems with existing algorithms
• Buffer sizes in the range of 20-30Mbytes are
  required
• Retains the VBR property of stored video
• Time complexity is of the order of O(N logN) and
  O(N 2)
• N is the number of frames

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Problem Statement

• Possibility of improvement
• When to change transmission rate ?

We wish to use best of both
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Visibility Polygon (VP) Algorithm

• Solution - VP algorithm
• Develop an algorithm based on visibility concept
• Developed by Subhash Suri ( John Hopkins, 1986)
• What is visibility ?
• Set of points that are visible from a given
  point in a region

visible to a
not visible to a
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Visibility Polygon (VP) Algorithm
Steps in VP algorithm 1) Given frame sizes
and client buffer size b. We construct the
feasible region P.
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Visibility Polygon (VP) Algorithm
Steps in VP algorithm 2) Triangulate the
feasible region P, let T represent the
triangulation of P.
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Visibility Polygon (VP) Algorithm
Steps in VP algorithm 3) Construct the dual
graph G of triangulated polygon.
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Visibility Polygon (VP) Algorithm
Steps in VP algorithm 4) Identify the shortest
path from first frame to last frame. 5) Compute
the windows, from which transmission plan is
obtained.
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Visibility Polygon (VP) Algorithm

• Complexity of VP algorithm
• Triangulation ----------------------------
  -------- O(N)
• Dual Graph Construction -------------------------
  --- O(N)
• Breadth First Search ----------------------------
  --- O(N)
• Visibility Polygon Windows computation ------
  
• Hence VP algorithm takes linear time

An improvement over the previous algorithms which
are O(N logN) and O(N2)
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Evaluation

• Comparison of OBA, Optimal and VP Algorithms
• Simulation model
• Use trace files of representative videos
• Parameters for evaluation
• Peak-rate bandwidth
• Number of rate changes
• Variation between rate changes
• Time complexity

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Evaluation
Peak-rate bandwidth
Peak-rate bandwidth
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Evaluation
Rate changes and variation between rate change
Intervals
Variation
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Evaluation

• Time complexity
• Measure the number of seconds for calculating
  transmission plan

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Evaluation
Experimental setup
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Evaluation
Validation of simulation model
Conservative results
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Evaluation

• Inputs
• Videos were selected to be representative with
  respect
• to length and subject material

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Evaluation
Peak-rate bandwidth
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Evaluation
Number of rate changes
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Evaluation
Amount of variation
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Evaluation
Time complexity
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Evaluation
What does all this mean to end users ?
If VP algorithms is used
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Summary and future work

• Summary
• Problems with efficiently transmitting stored
  (compression) video
• Reviewed OBA and Optimal algorithms
• New VP algorithm proposed
• Simulation results showed VP algorithm has
  better
• performance to its predecessors

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Summary and future work

• Future work
• To implement VP algorithm on an actual video
  server
• To study issues of multicast support of VP
  algorithm