QoE – Based Scheduling in WiMAX Networks

Kalypso Magklara

, Aggeliki Sgora

1,3

, Dimitrios D. Vergados

and Dimitrios J. Vergados

2,3

Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou St., GR-185 34 Piraeus, Greece

National Technical University of Athens, School of Electrical & Computer Engineering,

GR 157 80 Zografou, Athens, Greece

Technological Educational Institute of Western Macedonia, Department of Informatics and Computer Technology

GR-52 100 Kastoria, Greece

Keywords: WiMAX, Networks, Scheduling, rtPS, Quality of Service (QoS), Quality of Experience (QoE).

Abstract: Worldwide Interoperability for Microwave Access (WiMAX) networks provide wireless broadband internet

access, interoperability, while decrease the entrance barrier in mobile communications sector, and offer

services comparable to those of the emerging 4G technology. Τhe standard 802.16, upon which WiMAX

networks are based, has not designated any particular scheduling algorithm, allowing each provider to

develop its own. However, existing scheduling algorithms take into account the Quality of Service (QoS),

fairness and other parameters, but do not provide Quality of Experience (QoE). For this reason, in this paper

two different approaches are proposed in order to provide QoE, especially for the rtPS WiMAX service.

Simulation results show that by applying different policies the QoE provided to the WiMAX users is

improved.

1 INTRODUCTION

Current trends and future projections of the traffic

patterns show that multimedia traffic will soon

represent the largest proportion of wireless

bandwidth, replacing voice and data traffic.

However, since existing legacy wireless

technologies where deployed to support only voice

and data, advanced wireless networking

technologies are essential for the provision of the

multimedia traffic, since its nature differs

fundamentally from voice and data.

WiMAX (Worldwide Interoperability for

Microwave Access) (IEEE Standard 802.16-2004) is

an emerging wireless access technology that

provides high data rates and differentiated services

based on individual QoS (Quality of Service)

requirements (Lee an Song, 2010). In general the

IEEE 802.16 standard specifies the Unsolicited

Grant Service (UGS) to support real-time service

flows that have fixed-size data packets on a periodic

basis, the real-time Polling Service (rtPS) to support

real-time service flows that generate variable data

packets size that are transmitted at fixed intervals,

the extended rtPS (ertPS) to support real-time

service flows that generate variable data packets size

on a periodic basis, the non real-time Polling Service

(nrtPS) to support non real-time service flows that

require variable size bursts on a regular basis, and

the Best Effort (BE) designed for traffic where no

throughput or delay guarantees are provided. Table 1

presents the WiMAX Services and their

representative examples, as well as, the QoS

specifications of each service.

However, since the multimedia applications

require interaction with the users, existing QoS

requirements and performance metrics such as jitter,

packet loss, throughput etc, cannot guarantee the

user‘s satisfaction. For that reason the service

providers are now switching from QoS to Quality of

Experience (QoE), a term that encompasses both

QoS and the overall user satisfaction, which is

defined subjectively for each application, according

to the users’ expectations. This creates the

opportunity for users, service providers and network

operators to take advantage of the varying

bandwidth and delay requirements, in order to

improve the aggregate QoE in the system, and at the

same time to limit the operating costs.

The contribution of this paper consists of the

application of QoE to WiMAX networks, where

each user has different subjective requirements of

the system in terms of quality of service. For this

369

Magklara K., Sgora A., Vergados D. and Vergados D..

QoE – Based Scheduling in WiMAX Networks.

DOI: 10.5220/0004162103690373

In Proceedings of the International Conference on Signal Processing and Multimedia Applications and Wireless Information Networks and Systems

(WINSYS-2012), pages 369-373

ISBN: 978-989-8565-25-9

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

reason, in this paper two different approaches are

proposed in order to provide QoE, especially for the

rtPS WiMAX service. In the first policy the system

reduces the transmission rate of each connection if a

packet loss is occured, until each connection‘s

minimum allowable transmission rate is achieved. In

the second policy the transmission rate of the

connection is reduced if its packet loss is greater

than a threshold, while in the opposite case the

transmission rate is increased. Simulation results

show that by applying different policies the QoE

provided to the WiMAX users is improved.

Table 1: WiMAX Services –Applications and QoS

Specifications.

Service

Applications

Examples

QoS Specifications

UGS

Voice (VoIP)

without silence

suppression

Maximum sustained rate

Maximum latency tolerance

Jitter tolerance

rtPS

Voice (VoIP)

with silence

suppression

Maximum sustained rate

Minimum reserved rate

Maximum latency tolerance

Jitter tolerance

Traffic priority

nrtPS

File Ttransfer

Maximum sustained rate

Minimum reserved rate

Traffic priority

ertPS

Streaming audio

or video

Maximum sustained rate

Minimum reserved rate

Maximum latency tolerance

Traffic priority

Email,

Web browsing

Maximum sustained rate

Traffic priority

The paper is organized as follows: Section 2

presents related work concerning scheduling

algorithms for WiMAX networks. Section 3 presents

the proposed QoE policies while section 4 provides

details about the simulation parameters and presents

the obtained results from the proposed policies.

Finally Section 5 concludes the paper.

2 RELATED WORK

The IEEE 802.16 standard does not specify the

scheduling algorithm to be used. For that reason

several scheduling algorithms may be found in the

literature. Wu et al. (2012) considers two different

categories for scheduling in IEEE 802.16 related

research: the serviced-based and the connection-

based. In the first-category belong schemes which

allocate adaptive and corresponding scheduling

mechanism according to different types of services,

while the second category concerns adaptive

scheduling algorithms for every connection that do

not consider the service type of connections. A

comprehensive survey concerning MAC based QoS

implementations for WiMAX networks can also be

found in (Sekercioglu et al., 2009)

In addition several 802.16 modules for ns-2

simulation tools have been deployed (NIST WiMAX

Module, LRC WiMAX Module, Borin and Fonseca

2008).

Belghith and Nuaymi (2008) added QoS classes

to the ns-2 NIST WiMAX module, in addition to the

requirements of QoS management, unicast and

contention request opportunities mechanisms, and

scheduling algorithms for the UGS, rtPS and BE

QoS classes. Simulation results showed that their

UGS, rtPS and BE schedulers are in accordance with

the specification of QoS classes defined in IEEE

802.16 standard.

3 THE PROPOSED QoE

SCHEDULING POLICIES

In this section, the proposed QoE scheduling

policies are presented. As mentioned before due the

popularity of the video streaming service our interest

is focused on the rtPS.

3.1 Policy I

In the first policy a two level QoE is used, where

each user has an initial maximum transmission rate

and a minimal subjective requirement.

Each node starts to send traffic with the

maximum rate. If the per user packet loss over a

specified time interval exceeds a threshold, then

each user is checked. If the transmission rate of a

user is greater than its minimum, then its rate is

reduced by a given factor, otherwise it remains the

same. In the current simulation setup, the time

interval is set at 0.2 seconds, and the rate reduction

factor is 10%.

The rate reverts to its original maximum value

during the simulation; specifically it is restored

every 18 seconds. It was observed that it takes 15

seconds to reach the minimum requirements of all

the users.

3.2 Policy II

In the second policy a three level QoE is used, where

each user has an initial maximum transmission rate,

an average subjective threshold value and a minimal

subjective requirement.

WINSYS 2012 - International Conference on Wireless Information Networks and Systems

370

Similar to the policy, I the users start to send

traffic at their maximum rate. When the per user

packet loss exceeds a threshold chosen during the

implementation, over a specified interval then each

user is checked and if its transmission rate is higher

than its minimum rate, then the rate is reduced by a

given factor, otherwise it remains stable. However,

if the loss rate for a node is less than its threshold,

then the user is checked and if its transmission rate

is lower than its acceptable rate then its rate is

increased by the same factor; otherwise it remains

unchanged. In the current simulation setup, the rate

increment factor is also set at 10%. The threshold

may be selected before running a simulation as a

percentage of the data transmission rate of each user,

if value 20 is chosen then the threshold for packet

loss is 20% of the transmission rate.

4 PERFORMANCE EVALUATION

In this section, we evaluate the performance of the

two policies proposed in the previous section.

4.1 Simulation Environment

The WiMAX system operating in Point-to-

MultiPoint (PMP) mode was simulated by the well-

known ns-2.29 in which the QoS WiMAX module

proposed by Belghith and Nuaymi (2007) was

embedded that implements UGS, rtPS, and BE

schedulers in accordance with the specification of

the QoS classes defined in the IEEE 802.16

standard.

The topology of the network consists of one

wired node (the sink node) that communicates with

5 wireless nodes through a BS. The nodes are

located on a square grid, 250m x 250m. The

simulation time was set to 200s.

The PHY settings selected for the simulation are

given in Table 2. Moreover, the MAC frame

duration was set to 20 ms and the packet size 500

bytes.

Table 2: Simulation Physical Settings.

Parameter

Value

Channel

3.486e+9 GHz

Bandwidth frequency

5 MHz

Cyclic prefix

0,25

Propagation Model

Two Ray Ground

Antenna Model

Omni Antenna

Transmit Power

0.025 Watt

Receive Power Threshold

2.025e-12 Watt

Carrier Sense Power

Threshold

0.9 * Receive Power Threshold

Each node has different bandwidth requirements.

Tables 3 and 4 depict these different requirements.

Table 3: Policy I Node Parameters.

Flow data

rateNodes

Application rate

(KByte/ sec)

Minimum required

rate (KByte/ sec)

Node 1

Node 2

60.6

Node 3

Node 4

Node 5

60.6

Table 4: Policy II Node Parameters.

Flow

data rate

Nodes

Application

rate

(KByte/ sec)

Acceptable

rate

(KByte/ sec)

Minimum

required rate

(KByte/ sec)

Node 1

Node 2

60.6

Node 3

Node 4

Node 5

60.6

4.2 Simulation Results

In order to evaluate the performance of the proposed

algorithm, the following performance metrics are

considered:

 The average delay, i.e. the average amount of

time needed by each flow for transmitting its data.

 The average throughput per flow

 The packet loss percentage per flow.

In addition, we compared our results with the

ones obtained by the scheduler of the Belghith and

Nuaymi (2008), denoted as NIST WiMAX

scheduler. Figures 1-3 depict the obtained results for

the Policy I, labelled as QoE-based-scheduler in the

figures in comparison with the NIST WiMAX

scheduler while figures 4-6 depict the obtained

results for the Policy II for different thresholds,

denoted as QoE along with the corresponding

threshold, in comparison with the NIST WiMAX

scheduler.

As shown in the figure 1, by applying the policy

I the transmission delay of each flow is reduced.

Figure 2 depicts the throughput of each flow. As

it can been seen from the figure, the data rate of flow

1 for which the minimum user requirement is half of

the traffic that it produces, the rate is not reduced

tremendously but remains at the same levels as the

other flows using the QoE scheduler. However, the

rest of the flows alter their transmission rate to

approach the minimum requirements of each user in

order to reduce delays and packet loss rates.

QoE - Based Scheduling in WiMAX Networks

371

Delay vs Flow

0,000

50,000

100,000

150,000

200,000

250,000

300,000

350,000

flow 1 flow 2 flow 3 flow 4 flow 5

Flow

Delay

Nis t WiMAX Scheduler

QoE-based Scheduler

Figure 1: Per flow delay.

Throughput vs Flow

100

200

300

400

500

600

flow 1 flow 2 fl ow 3 fl ow 4 flow 5

Flow

Throughput kpbs

Nis t WiMAX Scheduler

QoE-based Scheduler

Figure 2: Throughput per flow.

Figure 3 shows the packet loss percentage per

flow. In all the flows except of flow 3, which has the

biggest flexibility concerning its transmission rate,

i.e. its minimum transmission rate is the ¼ of its

initial requirement, the packet loss percentage is

decreased in comparison with the one obtained from

the NIST WiMAX scheduler.

Packet Loss per Flow

0,00

10,00

20,00

30,00

40,00

50,00

60,00

flow 1 fl ow 2 flow 3 flow 4 fl ow 5

Flow

Packet Loss %

Nis t WiMAX Scheduler

QoE-based Scheduler

Figure 3: Packet loss percentage per flow.

Figures 4-6 depict the obtained results by

applying the policy with different thresholds.

As it can be seen from figure 4, when the

threshold is set to 10% the minimum average delay

per flow is achieved. For all the other thresholds, the

differences between the two schedulers are small. It

should be noted that when the threshold is set to

50%, the schedulers produce the same results.

Figure 4: Average delay per flow.

As concerns the throughput per flow, as figure 5

depicts, the selection of the threshold, as well as, the

range between maximum and minimum transmission

rate, defines the degree of reduction at the

throughput in comparison with the results obtained

by the NIST scheduler. The throughput for flows 1,

2 and 4 remained at the same levels as with the

NIST scheduler. As at policy I, the throughput of

flow 5 has the minimum reduction.

Figure 5: Throughput per flow.

Finally, as Figure 6 depicts the improvement is

obvious when using the scheduler based on QoE

with a threshold of 10% or 20% regarding the packet

loss percentage for all flows except for flows 3 and

Figure 6: Packet loss percentage per flow.

Delay per flow

0,000

50,000

100,000

150,000

200,000

250,000

300,000

350,000

flow 1

flow 2

flow 3

flow 4

flow 5

Flow

packet loss threshold

μ% of sendrate

Delay

NIST WiMAX

Scheduler

QoE μ = 10%

QoE μ = 20%

QoE μ = 30%

QoE μ = 40%

QoE μ = 50%

Throughput per flow

100

200

300

400

500

600

flow 1

flow 2

flow 3

flow 4

flow 5

Flow

packet loss threshold

μ%

of sendrate

NIST WiMAX

Scheduler

QoE μ = 10%

QoE μ = 20%

QoE μ = 30%

QoE μ = 40%

QoE μ = 50%

Throughput

kbps

Packet Loss per flow

0,00

10,00

20,00

30,00

40,00

50,00

60,00

flow 1

flow 2

flow 3

flow 4

flow 5

Flow

packet loss threshold

μ%

of sendrate

Packet Loσs %

NIST WiMAX

Scheduler

QoE μ = 10%

QoE μ = 20%

QoE μ = 30%

QoE μ = 40%

QoE μ = 50%

WINSYS 2012 - International Conference on Wireless Information Networks and Systems

372

5 CONCLUSIONS

Multimedia applications require interaction with the

users. For that reason the need for QoE provision is

imperative. In this paper, two different approaches

are proposed in order to provide QoE, especially for

the rtPS WiMAX service. Simulations results

showed that the use of different levels transmission

rate improves the QoE provided to the users. In the

first policy, where a two-level QoE is proposed the

packet loss and the delay are greatly reduced, but at

a slight cost on throughput. When the second policy

is applied the packet loss is further decreased

without affecting the delay and the throughput of

each node. It should be noted that the current results

were obtained without human participation, a factor

that will be considered in our future research work.

ACKNOWLEDGEMENTS

This work is partly supported by the University of

Piraeus Research Center (UPRC).

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