RESOURCE ALLOCATION ALGORITHMS FOR QOS OPTIMIZATION IN MOBILE WIMAX NETWORKS

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 5, No. 3, June 2013
DOI : 10.5121/ijwmn.2013.5306 77
RESOURCE ALLOCATION ALGORITHMS
FOR QOS OPTIMIZATION IN MOBILE
WIMAX NETWORKS
L. Nithyanandan1
and J. Susila @ Sandhiya2
1, 2
Department of Electronics and Communication Engineering,
Pondicherry Engineering College, Puducherry, India.
nithi@pec.edu
susila@pec.edu
ABSTRACT
WiMAX is based on the standard IEEE 802.16e-2009 for wireless access in Metropolitan Area Networks. It
is one of the solutions for 4G IP based wireless technology. WiMAX utilizes Orthogonal Frequency
Division Multiple Access which also supports Multicast and Broadcast Service with appropriate
Modulation and Coding Scheme. Presently, Scheduling and Resource allocation algorithm in Opportunistic
Layered Multicasting provides multicasting of layered video over mobile WiMAX to ensure better QoS.
Initially, the knowledge based allocation of subcarriers is used for scheduling. In addition, to reduce the
burst overhead, delay and jitter, SWIM (Swapping Min-Max) algorithm is utilized. Another promising
technology that can greatly improve the system performance by exploring the broadcasting nature of
wireless channels and the cooperation among multiple users is the Cooperative Multicast Scheduling
(CMS) technique. The simulation results show, Swapping Min-Max performs better with lesser number of
bursts, Zero jitter and with optimal throughput. The results with Cooperative Multicast Scheduling show
the enhanced throughput for each member in the Multicasting Scenario.
KEY WORDS
OFDMA, SWIM, CMS,Jitter, QoS, WiMAX.
1. INTRODUCTION
Last few years wireless communication has explored different technologies which include High
Speed Packet Access (HSPA), Universal Mobile Telecommunication System (UMTS), Long
Term Evolution (LTE), Evolution-Data Optimized (EV-DO), Wireless Interoperability for
Microwave access (WiMAX) and Multimedia Broadcast and Multicast Service (MBMS). Out of
these EV-DO and WiMAX has gained more popularity among broadband solution. The first
acceptable high speed mobile internet access around 5Mbps is EV-DO, however WiMAX
approaches to have higher speed as the networks continue to be deployed andcan deliver up to
70Mbps. In recent years, Broadband Wireless Access networks have been rapidly involved to
satisfy increasing user scalability and Quality of Service (QoS) [9].
The salient features of Mobile WiMAX are the higher data rate, mobility, scalability and Quality
of Service. Certain advanced features are the smart antenna technologies, multicast and
broadcast service and fractional frequency reuse.One of the main real time applications of
Mobile WiMAX is the Internet Protocol Television. Video, voice and data are all IP data
services, but each has its own Quality of Service (QoS) requirements. High availability,

78
sufficient guaranteed bandwidth, low transmission delay and jitter are the QoS requirement for
video services. To support QoS for various types of traffic, WiMAX medium access control
protocol defines bandwidth request-allocation mechanisms and five types of scheduling classes:
extended real time Polling Service (ertPS), Best Effort (BE), Unsolicited Grant Service (UGS),
non real time Polling Service (nrtPS) and real time Polling Service (rtPS).Both UGS and rtPS
are proposed to support real-time service generating packets periodically. While UGS is based
on constant Bit Rate (CBR) traffic such as VoIP, rtPS supports Variable Bit Rate (VBR) traffic
such as MPEG video. The core problem in mobile WiMAX network is the scheduling and
resource allocation. There are different modes for resource allocation such as partially used
Subcarrier (PUSC) and fully used subcarrier [8]. The PUSC mode is recommended for mobile
users scenarios. The Mobile WiMAX scheduling is designed to efficiently deliver broadband
services such as data, voice and video.
The rest of the paper is organized as follows. Section 2 discusses the literature survey. Section 3
elaborates transmission of the video over Mobile WiMAX. Swapping Min-Max algorithm is
discussed in section 4.Section 5describes the system model, Cooperative Multicast Scheme
(CMS) and analysis of the CMS scheme. Simulation results are analyzed in section 6 and finally
section 7 concludes the paper
2. LITERATURE SURVEY
Fen Hou, et al [5] developed an analytical model to evaluate the performance of the multicasting
scheme, in terms of service probability, power consumption and throughput of each group
member and multicast groups. C.W.Huang, et al [4], proposed a resource allocation algorithm
that provides enhanced QoS and efficiency for layered video Multicast. S.-M. Huang, et al, [3]
proposes a weakly consistent Opportunistic Multicast Scheduling (OMS) scheme for feedback
reduction. Chakchai So-In, et al, [2] introduces a technique called Swapping Min-Max (SWIM)
for UGS scheduling that not only meets the delay constraint with optimal throughput, but also
minimizes the delay jitter and burst overhead. Chih-Wei Huang, et al, [1] proposed
Opportunistic Layered Multicasting (OLM), a joint user scheduling and resource allocation
algorithm that provides enhanced quality for the layered Video.
3. WIRELESS IPTV OVER MOBILE WIMAX
Internet Protocol Television (IPTV) is widespread to deliver the content to users whenever they
want and wherever they are. Traditional wired access networks can deliver the contents only to
the stable users. Hence, a novel technology which can deliver the content to mobile users when
needed and mobile WiMAX is identified as a better choice. For real time application like IPTV
and VoIP a guaranteed QoS level is very important because these are CBR (Constant Bit Rate)
applications and are delay sensitive.
To support high data rate multicast and broadcast service in WiMAX, the techniques like OFDM
and OFDMA are used. Orthogonal Frequency Division Multiplexing (OFDM) is a method
which divides the bandwidth into multiple subcarriers at any given time. In mobile WiMAX,
Orthogonal Frequency Division Multiple Access (OFDMA) is used to access multiple users
simultaneously [6, 7]. The OFDMA symbol structure consists of Data, Pilot and Null sub-
carriers. The data and pilot subcarriers are clustered into subsets of subcarriers called sub
channels. The WiMAX OFDMA supports sub-channelization in both downlink and uplink.
There are two modes in WiMAX for sub-channelization, diversity and contiguous permutation.
The contiguous permutation draws the subcarriers as the block to form a sub-channel and the

79
diversity permutation draws sub-carrier pseudo-randomly to form a sub-channel. Due to the
frequency diversity and inter-cell interference averaging provided, the diversity permutation is
advised for mobile users. IPTV transmission over WiMAX requires coding techniques for high
quality video. In order to get high quality video, Scalable Video Coding (SVC) is used. SVC is
one of the challenging solutions to the problems caused by the characteristics of modern video
transmission systems. The word “scalability” refers to the elimination of parts of the video bit
stream in order to adapt it to the user’s need. The Scalable Video Coding is an extension of the
H.264/AVC standard. The motive of the SVC standardization is to enable the encoding and the
decoding of a high-quality video bit stream with the similar quality and the complexity that can
be achieved using the H.264/AVC design with the same quantity of data as in the subset bit
stream.
4. SWAPPING MIN- MAX ALGORITHM
The scheduler for rtPS service should meet the QoS criteria like Optimal system throughput ,
maximum latency guarantees, Minimal delay jitter and Minimal number of bursts in order to
reduce the MAP overheads[2].The resource is fixed in terms of number of slots per downlink
sub frame. This is denoted by variable number of slots. The number of bytes corresponding to a
slot depends upon the modulation and the coding. For the rtPS, at the connection setup, MSs
declares the total demand (denoted by the subcarriers for the required data size) and period
(denoted in terms of frames). For example, connection_1 asks for 540 bytes (1080 frames) every
4 sub carriers. WiMAX profiles specify a size of each frame about 5ms.The complexity of the
SWIM in the worst case is in the order of O (n2
log n), where n is the number of active
connections.
Complexity = O (allocation s + sorting) = O (n2
log n) (1)
And the Complexity = O (n2
) (2)
when the information is known about the number of flows.
The procedure for algorithm has two parts, Initialization with the optimal throughput/delay and
Resource swapping steps.The swapping procedure is as follows,
1. Determine the min_res connection and the max_res connection.
2. The two connections swap their resources such that min_res gives up its resources in the
current frameand it gains an equal amount of the resources in the forecoming frames.
3. The swapping procedure ensures that each burst is at least Minburstsize. (Minburstsizeis
considered here as 1).
4. If any connection has the resource equal to Minburstsize, they are excluded from swapping
(leaving Minburstsize value as non-zero).
The special cases which may exist are
1. The new max_res connection cannot accept any resources more than it needs and so the
min_res connection may not get eliminated.
2. If there is two or more max_res connection in the current frame, the connection which has the
resources higher in the next frame will be chosen.
3. If there is two or more max_res connection with same resources in the next frame, the
connection with more deadline with be selected.
4. If there are two or more min_res connections, the connection whose deadline is earlier will be
chosen.

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5. If there are two or more min_res connections with the same deadline, the connection withleast
resources in the next will be selected.
4.1 Swapping Min-Max examples
Table 1. Static Flows
C1 C2 C3 C4 C5
Data Size
(bytes)
62 35 500 60 580
Period
(frames)
260 160 900 240 1200
No of Subcarriers 4 4 6 6 12
Table 1 shows a simple example of 5 connections (C1-C5) and their demands (Data Size) in
bytes, Period in terms of WiMAX frames and the number of subcarriers. The total allocated slots
are 395. The throughput is optimal, that is (260*3) + (160*3) + (900*2) + (240*2) + (1200*1) =
4740 frames. Table 2 and 3 show the EQual Allocation (EQA) and Earliest Deadline First (EDF)
algorithms respectively. In EQA, the resource is allocated equally in every frame. In EDF, the
resource is allocated whose deadline is the first. In the next frame, the scheduler allocates the
remaining resources to meet the guaranteed throughput.
Table 2. EQA allocations
Time C1 C2 C3 C4 C5
0 65 40 150 40 100
1 65 40 150 40 100
2 65 40 150 40 100
3 65 40 150 40 100
4 65 40 150 40 100
5 65 40 150 40 100
6 65 40 150 40 100
7 65 40 150 40 100
8 65 40 150 40 100
9 65 40 150 40 100
10 65 40 150 40 100
11 65 40 150 40 100
Table 3. EDF allocations
Time C1 C2 C3 C4 C5
0 105 0 290 0 0
1 0 0 215 0 180
2 155 160 0 80 0
3 0 0 295 0 100
4 135 0 100 160 0
5 125 160 0 0 110
6 0 0 295 0 100
7 0 0 0 80 315

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8 25 0 210 160 0
9 0 0 395 0 0
10 130 160 0 0 105
11 105 0 0 0 290
Table 4 shows the initial swapping steps. In the first frame, the max_res connection is C3 and
the min_res connection is C2. Therefore, C2’s allocation in the frame is given to C1 and taken
back in the second frame. This results in C3 obtaining 150 + 40 = 190 and C2 obtaining 40 – 40
= 0 in the first frame. C3 obtains 150 – 40 = 110 and C2 obtains 40 + 40 in the second frame.
The resulting allocations are shown in Table 4 (a).Thus, the swapping has reduced the number of
bursts by one. In the next swapping step, C3 and C4 swap their allocations in the frame 1 and 2
resulting in the Table 4 (b) and swapping proceeds till the last frame. This results in the final
allocation shown in Table 5.
Table 4 SWIM initial steps
(a)
Time C1 C2 C3 C4 C5
0 65 0 190 40 100
1 65 80 110 40 100
2 65 40 150 40 100
(b)
Time C1 C2 C3 C4 C5
0 65 0 230 0 100
1 65 80 70 80 100
2 65 40 150 40 100
Table 5 SWIM Final Steps
Time C1 C2 C3 C4 C5
0 0 0 255 0 140
1 195 0 0 40 160
2 0 145 0 0 250
3 65 15 315 0 0
4 0 80 135 0 180
5 0 0 195 200 0
6 0 0 295 0 100
7 260 80 0 55 0
8 0 0 155 0 240
9 0 0 395 0 0
10 195 0 0 145 55
11 65 160 55 40 75

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4.2 Need for Cooperative Communication
SWIM performs efficiently, only when newly demanded resources are lesser than the available
number of slots. It leads to the complexity of the design when the number of information flow is
not known prior to the transmission. To meet all these considerations and support heavy traffic
Cooperative Multicast Scheduling Scheme is proposed particularly to cater live multicasting and
ensures higher throughput than existing multicast schemes.
5.COOPERATIVEMULTICAST SCHEDULING SCHEME
In Mobile WiMAX network consisting of a BSand multiple subscriber stations (SSs).IEEE
802.16 standards support mesh and Point-to-Multi Point (PMP) mode.
The TDD-OFDM/TDM MAC structure is considered as shown in Fig.1. The time domain is
divided into MAC frames withequal duration, each of which is composed of a downlink sub-
frame (DL sub-frame), an uplink sub-frame (UL sub-frame),a transmit/receive transition gap
(TTG), and a receive/transmittransition gap (RTG).
Fig.1 MAC structure for a conventional IEEE 802.16 network
In a conventional IEEE 802.16 network, SSs only receivedata from the BS in DL sub-frames. To
achieve cooperativemulticasting, the transmission burst assigned for multicast transmission is
divided into two phases as shown in Fig.2
Fig. 2 Cooperative multicast scheduling

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In multicast networks, for an IPTV service, an MGroup corresponds to a group of users
requesting the same TV channel. As Mobile stations could be a residential house or office
building consisting of multiple users accessing different channel simultaneously and thus may
belong to several MGroups. To select the appropriate MGroup, the schemes adopted are random
MGroup and channel-aware MGroup.
5.1 Multicast Group Selection
1. Random MGroup selection
The Base Station randomly chooses an MGroup for service with a pre-defined probability.
Each group is served with the probability of 1/T for achieving a good fairness performance. It is
easy to implement. To further improve the performance, the channel-aware MGroup selection is
used.
2. Channel-aware MGroup selection
This selection MGroup is considers the channel conditions into account on the group basis,
rather than a single group member. By using the multi-group channel diversity and taking into
account fairness, a criterion of MGroup selection is given by eqn. (3),
∗
= arg max (3)
and the normalized relative channel condition is given by eqn. (4)
=
∑ , ,⁄
(4)
Based on (3), the BS selects MGroup which has the maximum value of the normalized
relative channel condition.
5.2Co-operative Multicast transmission
After an MGroup is selected, the next step is to multicast data to all group members in the
chosenMGroup. To exploit the diversity gain in wireless channels, two phase transmission
technique is used to multicast the datain the downlink transmission, where every downlink burst
is divided into two phases. In Phase I, the BS multicasts data to all group members of MGroupi
at a high data rate of R1
suchthat only a particular portion of group members in MGroupi can
successfully decode the data and is shown in Fig.2. Due to the high data rate, the remaining
group members with unfavourable channel conditions may not be able to successfully decode all
the data in Phase I. Therefore, in Phase II, cooperative communication is utilized to promise
reliable transmission of the remaining group members.To investigate the performance of the Co-
operative Multicast Scheduling service probability of each MGroup, throughput of each user,
throughput of each MGroup and the whole network throughput is simulated[5].
5.3 Service Probability of MGroup i
Service probability is defined as the probability that the particular Multicast group is chosen to
be served when the system is stable. For the channel-aware MGroup selection, the service
probability (5) of the MGroup i is given by

84
πi = ∫ ( )!
∏ 1 − ∑
!,
∞
(5)
5.4 Throughput Analysis
The probability that a group member in MGroupican successfully receive the data in phase I is
≥ 2 − 1 =
/
(6)
If the Subscriber Station fails to receive the data in phase I. It is still possible for them to receive
the data in phase II and the probability is
≥ 2 − 1 = ∑ ∈ ,
≥ 2 − 1 | (7)
The throughput of subscriber’s station is given by,
( + )⁄ (8)
The group throughput of all the group members in MGroupiis given by,
ℎ = ∑ ℎ , (9)
In addition to the analysis based on channel capacity, the impact of the promising adaptive
modulation and coding (AMC) technique is also investigated. The transmission rate
corresponding to different modulation and coding levels is given by
= ⁄ (10)
The throughput achieved by the group member using AMC technique is given by,
ℎ , = , + 1 − , .*
∑ `
∈ ,
1 − ≥ 2 − 1 | (11)
6. SIMULATION RESULTS
The numerical results in terms of average number of bursts, delay jitter, delay of the EQA, EDF
and SWIM algorithms are simulated. The Swapping Min-Max algorithms are analyzed for base
and enhancement layers. Further, to effectively utilize the resources and to enhance the
throughput performance the multicast scheduling is carried out. The simulation parameters are
shown in Table 6 and the list of notations used is quoted in Table 7.

85
Table 6.performance evaluation parameters
PARAMETERS VALUE
SWIM
Frame Length 5ms
System Bandwidth 10MHz
CMS
Number of MGroups 10
Number of group members in MGroup 20
Frequency Band 3.5GHz
Noise Figure 7 dB
OFDM System duration 23.8µs
Table 7. Table of Notations
Notations Explanations
T The total number of MGroups
Gi The set of all members belonging to MGroupi
Ni The total number of group members in MGroupi
Gi
1
A set of members in MGroupi successfully receive data
E The received signal power
R The rate of the BS
1,2 Phase 1, Phase 2
T The transmission time
α Time ratio for multicast transmission
Thi
CMS
The group throughput of the MGroupi
In Information bits/ OFDM symbol
bn Lower boundaries of SNR
Ci Normalized relative channel conditions of the MGroupi
γ i , j Instantaneous channel conditions

86
Fig.3 Number of bursts for EQA, EDA and SWIM
Fig.4 Delay for the EQA, EDF and SWIM
Fig.3 shows the numerical results for the number of bursts in each frame. In equal allocation,
there is no reduction in number of bursts as it utilizes all slots in all Mobile Stations. Both the
EQA and EDA, minimizes the number of slots to reduce the MAP overheads.
From the Figs. 3, 4 and 5 it is inferred that the SWIM algorithm minimizes the number of bursts
with zero jitter and optimizes throughput as shown in Fig.6.
2 4 6 8 10 12 14 16 18 20 22 24
1
2
3
4
5
6
7
8
9
10
11
12
frames
NoofMSs
EQA
SWIM
EDF
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
11
12
Time(ms)
delay(ms)
EQA
SWIM
EDF

87
Fig.5 Delay Jitter for EQA, EDF and SWIM
Fig.6 Throughput of the SWIM algorithm for 10 users
Fig.4 shows the delay of EQA, EDF, and SWIM. The delays for EQA and SWIM are the same
which are equal to the periods. For EDF, even though delay is reduced when compared to the
other two algorithms it will in turn introduce the delay jitter as shown in Fig.5.But the delay
jitter is almost zero for SWIM.
Fig.7 shows the steady state probability that an MGroup is selected to be served at an arbitrary
frame. It is observed that each MGroup obtains almost the same service probability which
achieves the good fairness in terms of channel conditions.
1 2 3 4 5 6 7 8 9 10
-1
-0.5
0
0.5
1
1.5
2
Time(ms)
jitter(µs)
EQA
SWIM
EDF
1 2 3 4 5 6 7 8 9 10
0
2000
4000
6000
8000
10000
12000
No of users
Throughput(bytes/s)

88
Fig.7 Steady State Service Probability
Fig.8 Throughput for each Multicast group
1 2 3 4 5 6 7 8 9
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Index of MGroup
serviceprobability
1 2 3 4 5 6 7 8 9
10
10.5
11
11.5
12
12.5
13
13.5
14
Index of MGroups
GroupThroughput(Mbps)

89
Fig.9 Throughput for each Multicast group using AMC
Fig.8shows the throughput of the each Multicast group for which each MGroupis chosen based
on channel aware method. From the Fig.9it is clear that the throughput is still enhanced for the
each multicast group due to adaptive modulation and coding scheme used.
Fig.10 Throughput for each group members
1 2 3 4 5 6 7 8 9
17
18
19
20
21
22
23
24
Index of MGroups
GroupThroughput(Mbps)
2 4 6 8 10 12 14 16 18 20
0
5
10
15
20
25
Index of group members
Throughput(Mbps)

90
Fig.11 Normalized network throughput Vs Coverage ratio
Fig.10 shows the throughput for each group members and Fig.11 shows the normalized network
throughput. From the figure it is seen that the as the coverage area increases the throughput of
the network also increases.
7. CONCLUSION
WiMAX is one of the emerging technologies that can be used for all types of wireless services
(voice, data and multimedia, IPTV) which require higher capacity and data rates. In this paper,
Wireless IPTV istransmitted over Mobile WiMAX. To support various bandwidth requirements,
video streams are coded into base and enhancement layers by Scalable Video Coding (SVC)
technology. Efficient algorithms are carried out for the effective utilization of the resources. The
performance of the Swapping Min-Max algorithm minimizes the number of bursts and achieves
approximately zero jitter. Further, Cooperative Multicasting Scheduling is realized which still
optimizes throughputand exploits multi-channel diversity among the group members which are
involved in multicasting.
REFERENCES
[1] Chih-Wei Huang,Shiang-Ming Huang, Po-Han Wu, Shiang-Jiun Lin and Jenq-Neng Hwang,
“OLM: Opportunistic Layered Multicasting for scalable IPTV over Mobile WiMAX,” IEEE
Trans. Mobile Computing,vol.11,no.3,453-463,Mar. 2012
[2] Chakchai So-In, Raj Jain and Abdel-karim Al Tamini, “A Scheduler for Unsolicited Grant
Service (UGS) in IEEE 802.16e Mobile WiMAX Networks,” revised September, 2010.
[3] S.-M. Huang, J.-N.Hwang, and Y.-C. Chen, “Reducing Feedback Load of Opportunistic
Multicast Scheduling over Wireless Systems,” IEEE Comm. Letters, vol. 14, no. 12, pp. 1179-
1181, Dec. 2010.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Coverage Ratio
NormalizedNetworkThroughput

91
[4] Chih-Wei Huang,Shiang-Ming Huang, Po-Han Wu, Shiang-Jiun Lin, and Jenq-Neng Hwang,
“Layered Video Resource Allocation in Mobile WiMAX using Opportunistic Multicasting,”
Proc. IEEE Wireless Comm. And Networking Conf., Apr. 2009
[5] Fen Hou, Lin X Cai, Pin-Han Ho, XueminShen and Junshan Zhan, “A Cooperative Multicast
Scheduling Scheme for Multimedia Services in IEEE 802.16 Networks,” IEEE Transactions on
Wireless Communications, Vol. 8, no. 3, March 2009
[6] S. Deb, S. Jaiswa and K. Nagaraj, “Real-Time Video Multicast in WiMAX Networks,” Proc.
IEEE INFOCOM, pp. 1579-1587, 2008.
[7] Q. Qu and U. Kozat, “On the Opportunistic Multicasting in OFDM-Based Cellular Networks,”
Proc. IEEE Int’l Conf. Comm. (ICC ’08), pp. 3708-3714, May 2008
[8] WiMAX Forum, “Mobile WiMAX- Part I: A Technical Overview and Performance Evaluation,”
Aug.2006.
[9] J. Susila@Sandhiya and L.Nithyanandan, “Resource and QoS Optimization for multicasting in
Mobile WiMAX Networks,” Proceedings of IEEE International Conference on Communication
& Signal Processing – 2013, pp. 1536-1539, April 2013.
Authors Biography
L.Nithyanandan received Bachelor of Engineering from University of Madras in
1992, Master of Technology in 1999 and Ph.D degree in 2006 from Pondicherry
University. He is working as an Associate Professor of Electronics and
Communication Engineering, Pondicherry Engineering College, Puducherry,
India. He is a gold medalist in PG and has been awarded with chief minister medal
for his outstanding performance in PG. He has more than 22 publications in
National/ International Conferences and Journals. His areas of interest include
Sensor Networks, Telemedicine, Spread Spectrum Techniques and Wireless
Communication.
J.Susila@Sandhiya received her Bachelor of Technology Degree from
Pondicherry University in 2011 and Master of Technology Degree in 2013 in
Electronics and Communication Engineering,Pondicherry University. Her area of
interest includes wireless communication.

RESOURCE ALLOCATION ALGORITHMS FOR QOS OPTIMIZATION IN MOBILE WIMAX NETWORKS

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