An Integrated Source Transcoding and Congestion Control Paradigm for Video Streaming in the Internet

1
An Integrated Source Transcoding and Congestion
Control Paradigm for Video Streaming in the
Internet

• Proposed by
• R. Puri, K.W. Lee, K. Ramchandran and V.
  Bharghavan

Presented by Felix
2
Agenda

• Introduction
• LIMD/H Congestion Control
• MD-FEC Transcoder
• Performance Summary
• Discussion

3
Introduction

• More and more video traffic are using best-effort
  and unreliable channel service (eg. UDP) because
  of its low delay feature
• Explicit TCP-friendly Congestion Control policy
  is required to achieve fairness with other TCP
  flows

4
Introduction

• Two questions
• How to control the transmission rate?
• How to control the video source to adapt to the
  transmission rate?

5
LIMD/H Congestion Control

• LIMD (Linear Increase Multiplicative Decrease)
• r sending rate
• f fraction of packet loss in the last period
• If f 0, r ? r a
• If f gt 0, r ? r (1-b)
• Typically, (a, b) (1, 0.5)

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LIMD/H Congestion Control

• Problems of traditional LIMD
• React identically and aggressively to any kind of
  packet loss, both congestion induced and
  non-congestion induced.
• Even if the channel bandwidth is invariant, the
  sending rate fluctuates greatly

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LIMD/H Congestion Control

• LIMD/H (LIMD with History)
• h a history factor
• If f 0, r ? r a, and h ? 1
• If f gt 0, r ? r (1 bh), and h ? 2h
• b should be small to reduce the variation of
  sending rate

8
LIMD/H Congestion Control
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MD-FEC Transcoder

• Layered or Multi-Resolution (MR) source coding is
  a common coding method to provide
  quality/bit-rate scalability

Raw Video Stream
Base Layer
Multiresolution (MR) Source Coder
Enhancement Layer 1
Enhancement Layer 2
. .
Enhancement Layer N
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MD-FEC Transcoder

• Problem of MR coding
• Different quality layers have different
  importance ? Prioritized
• Eg. The client receiving packets of layers 0, 1,
  2, 4 will only get quality as just receiving 0,
  1, 2
• However, the network treats every packet, no
  matter which layer it belongs to, identically ?
  Non-prioritized
• Loss of lower layers packets makes some other
  successfully transmitted higher layers packets
  useless.
• Low robustness in a lossy channel

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MD-FEC Transcoder

• MD-FEC
• Transform a prioritized MR bit-stream to
  non-prioritized MD (Multi-Description) stream
  with additional redundancy (using FEC)

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MD-FEC Transcoder

• Step 1
• Partition a MR bit-stream to N layers and split
  the ith layer into i equal parts

R0
Ri-1
R2
Ri-2
R1
RN-1
RN-2


2
3

i

N
1
1
2
3

i-1
i
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MD-FEC Transcoder

• Step 2
• Adopt (N, i, N-i1) Reed-Solomon code to the ith
  layer and form N packets as follows

1
2

i

N
Packet 1
FEC
2

i

N
Packet 2
. . .
FEC
FEC

i

N
Packet i
FEC
FEC

FEC

N
Packet i1
. . .
FEC
FEC

FEC

N
Packet N
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MD-FEC Transcoder

• Optimization on Ri, i0N
• Notations
• qi(N) probability that i1 out of N packets are
  delivered to the destination
• D(r) Distortion function of rate r

Distortion

Rate

R0
R1
Ri
RN-1
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MD-FEC Transcoder

• Cont
• Problem statement
• Minimize the expected distortion ED
• E is the distortion encountered when the source
  is represented by zero bits

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MD-FEC Transcoder

• Cont
• The total rate Rt equals
• Thus constraints to the optimization problem are

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Performance Summary

• Simulations have been done to show the variation
  of sending rate and PSNR upon variation in
  network capacity and random losses
• MD-FEC can maintain a smooth PSNR
• LIMD/H can adapt to the variation of network
  bandwidth quickly while reducing the rate
  fluctuation induced by random losses and channel
  probing

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Discussion

• The MD-FEC is a novel technique to add robustness
  to many layer coding schemes
• LIMD/H congestion control mechanism can provide
  low variation in transmission rate while
  guaranteeing inter-traffic fairness
• However, the delay caused by explicit end-to-end
  feedback and FEC operations may affect the
  performance of the system