Transcript Slide 1

VIDEO STREAMING OVER
COGNITIVE RADIO NETWORKS
CMPT 820 : Final Project
Fall 2010
Presented by: Azin Dastpak
Outline
2





Introduction
Problem Statement
Solution
Evaluation using Simulation
Conclusion and Future Works
Introduction
3

Current wireless networks are regulated by fixed
spectrum assignment policy.
Fixed Spectrum Assignment policy
spectrum white spaces
Inefficient spectrum utilization

Cognitive radio network is :
A
new paradigm that provides the capability to share
or use the spectrum in an opportunistic manner.
Cognitive Radio Network Components
4

Primary network
 Primary
users
 Primary base station

Secondary network
 Secondary
users
 Secondary base station
 Spectrum broker
A
scheduling server that shares the spectrum resources
between different cognitive radio networks
Cognitive Radio Network Components
5
Components of Cognitive Radio Network
Streaming Video over Cognitive Radio
Network
6



Broadcast/Multicast Video over a Network
Primary user operates in a specified frequency
band
Is the dedicated bandwidth enough for desired
video quality
Multimedia is one bandwidth-hungry application
that can fully utilize the potentials of Cognitive
Radio Networks.
Problem Statement
7





CRN co-exists with a Primary Network with channel
bandwidth Cp
Secondary user has its own dedicated bandwidth
Access Rule for channel: TDMA
Video Encoding : FGS
Goal: Maximize perceived video Quality by users
Solution
8

FGS encoder, encodes the video file into two stream

Base layer

Enhancement layer


Base Layer frames delivery shall be guaranteed


Quality refinement is proportional to the number of bits received
Transmit base layer frames over the dedicated primary channel
Transmit Enhancement layer over Cognitive Radio Network

How many bits shall be allocated per frame?
Secondary channel’s availability is dynamic.
Need to predict future secondary channel states
Solution
9

Model the Primary Channel State with a two-state
Markov Model
 State
A: Channel is Available
 State B: Channel is Busy


Arrival and departure of the primary network as a
continuous Poisson process with rate μ
Negatively Exponential Service time with mean
time β = 1/μ.
Solution
10
Two-state discrete Markov Chain Model
Solution
11





Estimate the average Available time (TA) and (TB)
How many bits shall be allocated to each frame
with the goal to maximize the quality of perceived
video overall the time period T = (TA) + (TB)
Multiplying this average time by frame-rate we'll
get the number of the frames that will be sent over
the time that channel is available.
Considering the capacity of the available channel
we have a bit budget
Allocate equal number of bits to each frame
Evaluation using Simulation
12




Implemented a discrete-event simulator in Java to
simulate the behavior of Secondary channel
according to Markov Chain Model.
Assumed the values of μ and λ to be 0.5 each.
The times of entering each state is inserted into a
Calendar.
At the time of each event, the event will be
extracted from the Calendar and average idle and
busy times will be estimated.
Evaluation using Simulation
13

Compute the estimated average time:

avgIdletime = α(avgIdletime) + (1-α)(observedIdletime)

Allocate equal number of bits to each frame
At the end of each period of time (T = (TA) + (TB) ),
we measure the number of lost frames.

Evaluation using Simulation
14
Average time estimation
50
45
40
number of frames
35
30
25
lost frames
underutilization
20
15
10
5
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91
Time period #
Number of lost frames, estimating average time with different parameters
Evaluation using Simulation
15
Weighted average time estimation (alpha = 0.5)
60
50
number of frames
40
30
lost
underutilization
20
10
0
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94
time period #
Number of lost frames, estimating average time with different parameters
Evaluation using Simulation
16
Weighted average time estimation (alpha = 0.2)
90
80
60
50
lost
40
underutilization
30
20
10
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
67
69
71
73
75
77
79
81
83
85
87
89
91
93
95
number of frames
70
time period #
Number of lost frames, estimating average time with different parameters
Conclusion
17


At the beginning of simulation the algorithm is still in
initial state and number of lost frames is high or we
are faced with channel underutilization.
Experimenting with different values for α, (α = 0.2)
was more suitable to for estimation of each state’s
time length, leading to less frame loss.
Future Work
18




Studying different real channels to assign close-toreal life values to channel characteristics
Assign optimal number of bits to each frame to
minimize frame loss while maximizing the perceived
quality over time T.
Secondary channel may follow a more complicated
model, (HMM).
Try to observe the behavior of channels pick the
most appropriate model.
References
19






[1] I. Akyildiz, W. Lee, M. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum
access/cognitive radio wireless networks: a survey,” Computer Networks, vol. 50, no. 13, pp.
2127–2159, 2006.
[2] Q. Zhao and B. Sadler, “A survey of dynamic spectrum access,” Signal Processing
Magazine, IEEE, vol. 24, no. 3, pp.79–89, 2007.
[3] Q. Zhao, S. Geirhofer, L. Tong, and B. Sadler, “Opportunistic spectrum access via periodic
channel sensing,” Signal Processing, IEEE Transactions on, vol. 56, no. 2, pp. 785–796, 2008.
[4] A. Fattahi, F. Fu, M. van der Schaar, and F. Paganini, “Mechanism-based resource allocation
for multimedia transmission over spectrum agile wireless networks,” Selected Areas in
Communications, IEEE Journal on, vol. 25, no. 3, pp. 601–612, 2007.
[5] H. Mansour, J. Huang, and V. Krishnamurthy, “Multi-user scalable video transmission control
in cognitive radio networks as a Markovian dynamic game,” in Decision and Control, 2009
held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of
the 48th IEEE Conference on. IEEE, 2010, pp. 4735–4740.
[6] D. Hu, S. Mao, Y. Hou, and J. Reed, “Scalable video multicast in cognitive radio networks,”
Selected Areas in Communications, IEEE Journal on, vol. 28, no. 3, pp. 334–344, 2010.
Questions?
20