Download An Analytical Solution to Video Streaming in Mobile Ad hoc Networks

ACE: An Analytical Solution to Video Streaming
in Mobile Ad hoc Networks
Jeremiah Deng
Information Science / Telecommunications Programme
University of Otago
September 25, 2014
Acknowledgement
I
Dr. Yuwei Xu
I
Co-supervisors: Prof. Martin Purvis, Dr. Mariusz
Nowostawski
Outline
Introduction
Video Streaming
ACE: Modeling
Simulations
Admission Control
Mobility Support
Outline
Introduction
Video Streaming
ACE: Modeling
Simulations
Admission Control
Mobility Support
Background
I
Wireless devices are everywhere
Background
I
Wireless devices are everywhere
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Background
I
Wireless devices are everywhere
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
Background
I
Wireless devices are everywhere
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
Mobile ad hoc networks can be established on the fly:
Background
I
Wireless devices are everywhere
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
Mobile ad hoc networks can be established on the fly:
I
No pre-existing infrastructure required
Background
I
Wireless devices are everywhere
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
Mobile ad hoc networks can be established on the fly:
I
I
No pre-existing infrastructure required
Multi-hop wireless links
Background
I
Wireless devices are everywhere
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
Mobile ad hoc networks can be established on the fly:
I
I
I
No pre-existing infrastructure required
Multi-hop wireless links
Support mobility
Background
I
Wireless devices are everywhere
I
I
I
Mobile ad hoc networks can be established on the fly:
I
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
No pre-existing infrastructure required
Multi-hop wireless links
Support mobility
Many application:
Background
I
Wireless devices are everywhere
I
I
I
Mobile ad hoc networks can be established on the fly:
I
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
No pre-existing infrastructure required
Multi-hop wireless links
Support mobility
Many application:
I
Personal area networking: entertainment, health monitoring ...
Background
I
Wireless devices are everywhere
I
I
I
Mobile ad hoc networks can be established on the fly:
I
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
No pre-existing infrastructure required
Multi-hop wireless links
Support mobility
Many application:
I
I
Personal area networking: entertainment, health monitoring ...
Military environments
Background
I
Wireless devices are everywhere
I
I
I
Mobile ad hoc networks can be established on the fly:
I
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
No pre-existing infrastructure required
Multi-hop wireless links
Support mobility
Many application:
I
I
I
Personal area networking: entertainment, health monitoring ...
Military environments
Civilian environments: e.g. taxi cab network, boats, aircraft
Background
I
Wireless devices are everywhere
I
I
I
Mobile ad hoc networks can be established on the fly:
I
I
I
I
Mobile phones, tablets, wrist-watches, sensors, tags ...
Wi-Fi, Bluetooth, ZigBee ...
No pre-existing infrastructure required
Multi-hop wireless links
Support mobility
Many application:
I
I
I
I
Personal area networking: entertainment, health monitoring ...
Military environments
Civilian environments: e.g. taxi cab network, boats, aircraft
Emergency operations: search-and-rescue
Challenges
I
Limited wireless transmission range
I
Broadcast nature of the wireless
medium
I
Packet losses due to transmission
errors
I
Mobility-induced route changes
I
Mobility-induced packet losses
I
Battery constraints
I
Ease of snooping on wireless
transmissions (security hazard)
Reactive Routing as a Solution
I
Find a route only when transmission is needed
I
Controlled flooding of Routing Request (RREQ) and Routing
Reply (RREP) frames
I
Route time-out in case of routing errors
I
E.g.: Ad hoc On-Demand Distance Vector (AODV)
AODV in Action
I
Nodes propagate Source’s RREQ until reaching Destination
I
Nodes set up backward links upon hearing RREQ
I
Destination propagates RREP back to Source
I
Nodes set up forward links from RREP; Route now formed.
Outline
Introduction
Video Streaming
ACE: Modeling
Simulations
Admission Control
Mobility Support
Video Streaming
Like many other network
applications, video streaming
relies on the layered TCP/IP
stack:
I
Application sends/receives
video packets in continuous
flows.
I
Null transport layer: no flow
control or error control
I
Network layer handles
routing
I
Technologies (Ethernet,
Wi-Fi etc.) deal with
transmission.
Video Streaming in a MANET
I
Neighbour nodes contend for medium access when
transmitting.
I
Channel quality changeable and subject to weather conditions
I
Noise and interference may cause transmission errors and
retransmission attempts.
I
Mobility can cause routing disruptions.
I
Also, data rates (‘bandwidth’) are limited.
/ Factors related multiple layers lead to worse streaming
performance.
I
Solutions: shortest path (AODV), link quality (ETX),
load-balancing (ALARM, NLR), location-assisted (SPEED) ...
Outline
Introduction
Video Streaming
ACE: Modeling
Simulations
Admission Control
Mobility Support
Queueing Model: Jackson Network of M/M/1/K
interference
K
......
inflow
outflow
λ
λ
µ
d
reTx
I
We assume each node can be modelled as an M/M/1/K
queueing system with
I
I
I
I
Poisson arrival process
Processing time in exponential distribution
System capacity limited to K packets
The network is modelled as a Jackson network where
queueing models can be analysed individually.
M/M/1/K Analysis
I
State transition diagram
I
λ: arrival rate, µ: processing rate
λ
Utility ρ = < 1
µ
Steady state solutions:
I
I
I
K
X
pn = 1
n=0
I
λ
pn = ( )n p0 , 0 ≤ n ≤ K
µ
M/M/1/K Results
I
Probability that an arriving customer is rejected is simply pK .
I
Rejection rate is therefore λd = pK λ.
I
Time in system
T =
I
1
µ − λ + λd
The expected load Q
Q =
=
ρ
(K + 1)ρK +1
−
1−ρ
1 − ρK +1
λ2 µK + K λK +2 − λK +2 − K µλK +1
.
µK +2 − µK +1 λ − µλK +1 + λK +2
Our Problem
interference
K
......
inflow
outflow
λ
λ
µ
d
reTx
I
In reality, we know λ, but not µ ...
I
We do know Q (Q ≤ K ), and λd
I
The question: how to estimate µ, so that the processing time
for each node can be derived.
The Solution
I
Re-checking on the packet dropping rate
λd = λPK =
we have
µK +1 =
λK +1 µ − λK +2
,
µK +1 − λK +1
λK +1 µ − λK +2
+ λK +1 .
λd
The Solution
I
Re-checking on the packet dropping rate
λd = λPK =
we have
µK +1 =
I
λK +1 µ − λK +2
,
µK +1 − λK +1
λK +1 µ − λK +2
+ λK +1 .
λd
Re-introducing Q:
2λQ
λ2 Q λ2
Q 3 3
(µ −λ )+(K −
)(µ2 −λ2 )+(
− +λ−K λ)(µ−λ) = 0.
λd
λd
λd λd
The Solution
I
Re-checking on the packet dropping rate
λd = λPK =
we have
µK +1 =
λK +1 µ − λK +2
,
µK +1 − λK +1
λK +1 µ − λK +2
+ λK +1 .
λd
I
Re-introducing Q:
I
2λQ
λ2 Q λ2
Q 3 3
(µ −λ )+(K −
)(µ2 −λ2 )+(
− +λ−K λ)(µ−λ) = 0.
λd
λd
λd λd
Since µ > λ, we have a simplified quadratic equation
Q 2
Qλ
λ2
µ + (K −
)µ + (λ −
) = 0.
λd
λd
λd
The Solution
I
Re-checking on the packet dropping rate
λd = λPK =
we have
µK +1 =
λK +1 µ − λK +2
,
µK +1 − λK +1
λK +1 µ − λK +2
+ λK +1 .
λd
I
Re-introducing Q:
I
2λQ
λ2 Q λ2
Q 3 3
(µ −λ )+(K −
)(µ2 −λ2 )+(
− +λ−K λ)(µ−λ) = 0.
λd
λd
λd λd
Since µ > λ, we have a simplified quadratic equation
I
Q 2
Qλ
λ2
µ + (K −
)µ + (λ −
) = 0.
λd
λd
λd
Hence the solution:
s
λ2 − λd λ λd K − Qλ 2 λd K − Qλ
µ=
+
.
−
Q
2Q
2Q
ACE Routing Metric
I
Analytical Capacity Estimation (ACE) of a node defined as
∆ = µ − λ + λd
I
It is the reciprocal of “time in system” for an M/M/1/K
I
Smoothed, local routing cost at a node n:
(n)
Ci
I
(n)
= αCi−1 + (1 − α)
1
(n)
∆i
Routing metric for a potential route Ω
X (n)
Ctotal =
Ci
n∈Ω
I
Our goal is to find a route with the minimal C.
ACE Protocol in a Nutshell
1. Source node floods out RREQ with a metric field and listens
to RREP;
2. Intermediate node adds its ACE value to the RREQ metric
and propagates it further; sets up reverse links;
I
RREQ with metrics larger than the best will be dropped;
otherwise update the best with current metric.
3. Destination node copies RREQ metric into RREP and sends it
back via reverse links through intermediate nodes;
I
First RREQ arrival starts a timer; time-out to send back RREP
with the best metric.
4. Intermediate nodes and Source receive RREP; set up forward
links.
5. Data packets can now follow the route formed by forward
links.
Outline
Introduction
Video Streaming
ACE: Modeling
Simulations
Admission Control
Mobility Support
Simulation Settings
I
Network Simulator 2 (NS-2) + EvalVid in an IEEE 802.11b/g
networks of grid topologies: 5 × 5, 7 × 7, 9 × 9, 15 × 15
Parameters
Distance between two neighbours
Antenna Type
Standard
Transmission Range
Transmission Rate
Packet Size
Queue Size
Video Format
Duration
Number of Streams
Minimum number of hops
Values
150m
Omnidirectional
802.11b
250m
11 Mbits/s
1024 bytes
50 packets per node
H.264/MEPG4
29 ∼ 66s
3∼7
4
Poisson Inflow / Outflow
Histogram with Poisson PDF
0.020
Probability
0.000
0.010
0.020
0.000
0.010
Probability
0.030
0.030
Histogram with Poisson PDF
160
180
200
220
240
160
200
220
240
240
220
200
160
180
Sample Quantiles
220
200
180
160
Sample Quantiles
180
number of outward packets
240
number of incoming packets
160
180
200
Theoretical Quantiles
220
240
160
180
200
Theoretical Quantiles
220
240
Chi-Square Test Results
For video ‘Highway’:
Node
N30
N36
N31
DF
6
5
6
Inflow
χ2
12.1596
7.3989
11.1698
DF
6
6
6
Outflow
χ2
12.1198
10.6659
10.9301
For video ‘Grandma’:
Node
N34
N41
N49
DF
5
5
5
Inflow
χ2
5.2467
4.3583
1.2288
Outflow
DF
χ2
5
5
5
1.4930
6.0237
4.3098
Our assumption on Poisson traffic stands on the significance level
of α = 0.05.
Comparison Study
I
Protocols in comparison: ACE, NLR, ETX, ALARM, AODV
I
Performance evaluation:
I
I
I
I
Streaming quality: Peak Signal-to-Noise Ratio
Average number of “good streams”
Packet drop ratio
Average delay per packet
ETX
1
0
2
−
−
AODV
−
ETX
2
−
ALARM
0
−
Number of video with good quality
4
NLR
1
7 X 7 Topology
ACE
2
− −
AODV
−
3
ETX
−
ALARM
4
NLR
− −
Number of video with good quality
5 X 5 Topology
ACE
AODV
−
ALARM
3
NLR
I
ACE
Number of video with good quality
# of Good Streams Compared
Video streams in good quality (PSNR ≥ 32 dB)
9 X 9 Topology
4
3
−
−
1
−
0
45
40
35
30
25
25
25
20
20
AODV
45
ETX
45
ALARM
50
NLR
50
Average quality of video(dB)
7 X 7 Topology
ACE
AODV
30
ETX
35
ALARM
40
NLR
50
Average quality of video(dB)
5 X 5 Topology
ACE
AODV
ALARM
ETX
NLR
I
ACE
Average quality of Video(dB)
Average Streaming Quality
Average PSNR for all streams
9 X 9 Topology
40
35
30
Packet Drop Ratio
0.25
0.15
0.10
0.05
AODV
ALARM
ETX
NLR
0.00
ACE
Packet loss rate (%)
0.20
A Bigger Network - PSNR
I
15 × 15 running IEEE 802.11g
PSNR Scalability
A Bigger Network - Packet Drop Ratio
I
15 × 15 running IEEE 802.11g
A Bigger Network - Delay
I
15 × 15 running IEEE 802.11g
Outline
Introduction
Video Streaming
ACE: Modeling
Simulations
Admission Control
Mobility Support
ACE-AC: Principle
I
I
Unlimited accommodation of calls will lead to a break-down.
Our solution:
I
I
I
I
λ
µ
Upon receiving a new request with advertised λ, estimate new
utilisation ρ0
Threshold 1: ρG = 0.91 (‘Good’ quality, drop ratio Pd =0.1%)
Threshold 2: ρA = 1 (‘Acceptable’ quality, drop ratio Pd =5%)
Each node monitors its utilisation ρ =
Admission vs. Utilisation
0
40
80
time(second)
(A)
1.1
1.0
0.7
0.8
0.9
Utilisation ratio − (ρ)
1.0
0.7
0.8
0.9
Utilisation ratio − (ρ)
1.0
0.9
0.8
0.7
Utilisation ratio − (ρ)
adding
6th video
1.1
adding
5th video
1.1
adding
4th video
0
40
80
time(second)
(B)
0
40
80
time(second)
(C)
Admission Control - PSNR
I
802.11g network of 15 × 15 topology
I
ACE-AC maintains high/good quality of existing streams
while new streams are possibly added.
Frame Quality Comparison
Video frame at 43 sec., 4th stream. Clockwise: 5 streams; 6
streams with AC; 6 streams without AC.
Outline
Introduction
Video Streaming
ACE: Modeling
Simulations
Admission Control
Mobility Support
Introducing Route States
p
1-p
1-q
Connected
Broken
q
Probabilities of ‘Connected’ and ‘Broken’ states: πc and πb .
πc = πc (1 − p) + πb q,
πb = πc p + πb (1 − q),
πc + πb = 1.
Solutions:
q
p+q
p
πb =
p+q
πc =
A Simple Treatment
According to Samar & Wicker (2004), when node speed v is low
(< 5 m/s):
I
The link lifetime has an almost linear relationship to v .
I
The new link arrival rate remains stable.
Hence we can assume p = kv , where k is a constant.
πc =
1
q
=
,
kv + q
γv + 1
with γ = k/q. And the mobility-modified capacity is
∆m = ∆
1
γv + 1
i.e. nodes with lower speeds are preferred.
Mobility Simulation Setup
I
Test on impact of mobile population size, random speed
(0.5,1) m/s:
I
I
I
I
I
Minority: 20% nodes moving
Medium: 50% moving
Majority: 80% moving
50 simulations run for each scenario with the same random
initial topology
Impact of speed: minority setting, nodes picking a random
speed 0 - 5 m/s
Performance Comparison
3.0
3.0
1.0
ACEm
ACE
NLR
ETX
ALARM
AODV
2.0
Nf
1.5
2.5
1.5
0.5
1.0
0.5
0.0
Np
2.0
2.5
ACEm
ACE
NLR
ETX
ALARM
AODV
1
2
3
4
5
Velocity (m/s)
Number of videos in perfect quality
0.0
minority
medium
majority
Motion scenarios
Number of failed videos
Ongoing / Future Work
I
Opportunistic routing in delay-tolerant networks (DTN)
I
Energy-efficient routing in MANET / DTN
I
Optimisation of wireless sensor networks with
energy-harvesting