Transcript Slide 1

School of Computing Science
Simon Fraser University
Multiplexing of Variable Bitrate Scalable
Video for Mobile Broadcast Networks
Project Presentation
Farid Molazem
Cmpt 820
Fall 2010
Mohamed Hefeeda
Outline
 High level description of the problem
 Overview of scalable video coding
 Formulation of the problem
 Problem solution
 Evaluation and results
Mohamed Hefeeda
High level description of the problem
 The problem we study here is broadcasting several
variable bitrate video streams to a large number of
mobile receivers while maximizing performance
metrics of bandwidth utilization and energy saving
 Challenges
- Variability in the bitrates of video streams
- Small buffer sizes of mobile receivers
- Energy constraints for mobile devices
Mohamed Hefeeda
Scalable Video Coding
 Scalable video coding
- Temporal scalability
- Spatial scalability
- Quality scalability
Mohamed Hefeeda
Quality Scalability
 Quality scalability could be considered as a special case
of spatial scalability
 We could have
- Fine Grain Scalability
- Coarse Grain Scalability
- Medium Grain Scalability
Mohamed Hefeeda
Problem Formulation
 Problem: Broadcasting S scalable video streams from a
base station to a large number of mobile receivers over
a wireless medium
 Notation:
- There are S video streams
- Each frame video stream s has a base layer and Q s MGS layers
- Each video stream has I frames
-
li , s ,k
Indicates the size of layer k of
frame i of stream s
- Each stream is coded at F frame-per-second
Base layer
li , s ,k
Frame i of stream s
Mohamed Hefeeda
Problem Formulation
 Bandwidth Utilization
- The fraction of video frames received at the decoder before
their decoding deadline
S
ns

 
 Energy Saving:
s
bj / R
s 1 j 1
I/F
- The fraction of time the receivers can put their wireless
receivers into sleep
- We use the average energy saving among all video streams
  (  s 1  s ) / S
S
Mohamed Hefeeda
Problem Formulation
 The average quality of all transmitted frames is shown
by 
- We use peak-signal-to-noise-ration (PSNR) as a quality metric
PSNR  10 log
S
10
ns
(
MAX
s
hk
s
ui
s  1 k  1 i  g ks q  1

Mohamed Hefeeda
ns
k 1
)
MSE

 
2
I
s
bk
i ,s ,q
Problem Solution
 We solve the problem in two steps
- First Step:
• We solve the problem as if the video streams are not
scalable
 We show that energy saving and bandwidth utilization are
not independent
» We control transmission by considering buffer level
in the video streams
» Define a parameter that tells us when to schedule a
burst: 𝛼 × 𝐵𝑢𝑓𝑓𝑒𝑟
time
Mohamed Hefeeda
Problem Solution
- Second Step:
• Consider only base layer of video streams
• Add burst to S2 until it reached threshold
• Now we add back the quality layers as long as we have
available bandwidth in our rescheduling window
s1
s2
Below threshold
Rescheduling window
Mohamed Hefeeda
time
Problem Solution
- Second Step:
• Define a threshold for buffers, 𝜷
 If the buffer level is less than 𝜷 × 𝑩𝒖𝒇𝒇𝒆𝒓 we start
adjusting quality layers of video streams
s1
s2
Below threshold
Rescheduling window
Mohamed Hefeeda
time
Problem Solution
- Second Step:
 We have a number of frames
 Each frame has a number of quality layers
 Selecting each quality layer consumes some space and
provides some quality value
 Which quality layers to choose?
» 0-1 multiple choice knapsack
» NP-Complete
Mohamed Hefeeda
Problem Solution
- Second Step:
 Use an approximation algorithm
 Scale the table : 𝜃 =
 𝑞𝑖,𝑗 =
𝜖 𝑄𝑚𝑎𝑥
𝑓𝑡𝑜𝑡𝑎𝑙
𝑞𝑖,𝑗
𝜃
Mohamed Hefeeda
Evaluation
 Settings
- We set the modulator to use 16-QAM (Quadrature Amplitude
Modulation)
- 10MHz radio channel
- Overhead To=100ms
 Video streams
- 16 video streams of different categories of: sport, tv game show,
documentray, talk show and have very different visual
characteristics
- Bitrates ranging from 250 to 768 kbps
- We created video streams with different MGS layers and the
trace file for each stream using “BitStreamExtractorStatic” tool
provided by JSVM
- We used “PSNRStatic” to determine the PSNR value of each MGS
layer of each video stream
Mohamed Hefeeda
Results
 Dropped Frame
Mohamed Hefeeda
Results
 Resulted Quality
Mohamed Hefeeda
Conclusions
 Having a small lookahead window, we can have an
approximation algorithm to reduce dropped frame
using quality scalability characteristics of scalable
video streams
 Operator can adjust the chance of dropping frame by
adjusting the threshold for buffer levels
- Reducing the threshold lowers the chance of dropping frames
at a cost of loosing some quality in video frames
Mohamed Hefeeda
Thanks You
Mohamed Hefeeda