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Power-Driven VoIP Quality Adaptation Over

WLAN in Mobile Devices

Is-Haka Mkwawa and Lingfen Sun

School of Computing and Mathematics University of Plymouth, Plymouth, PL4 8AA,UK

(Is-Haka.Mkwawa,L.Sun)@plymouth.ac.uk

Abstract—Network parameters such as packet loss and delay have been extensively investigated as the major factors in deter-mining when and how to carry out VoIP quality adaptation to enhance quality of experience. However, a little effort has been put into investigating the impact of non-network parameters such as limited battery resources in mobile devices on the VoIP QoE. Battery life which is not long enough to complete VoIP communication session will adversely affect users’ QoE. In order to mitigate battery life limitation, this paper proposes VoIP quality adaptation scheme whereby an acceptable quality is maintained by changing video send bitrate in order to conserve power and hence, prolong VoIP communication session.

Through experiments, preliminary results have shown the ef-fectiveness of the proposed scheme in terms of power saving while maintaining acceptable QoE. The power saving was between 10-30% of the total system power.

Index Terms—QoE, IMS, SIP, Android, WLAN, Quality Adap-tation, energy, power

I. INTRODUCTION

Mobile phones are now packaged with a range of multi-media applications such as YouTube, Google Talk and Skype. However, these applications consume considerable amount of power especially when video communication is involved [1], [2]. While mobile devices technologies such as CPU and storage capacity have immensely evolved over the past few years, unfortunately, technologies to extend battery life has not experienced the same evolution. This limitation has prompted an extensive research into power conservation techniques such as power aware operating systems and mobile resources management [3].

The Quality of Experience (QoE) in VoIP applications is a subjective measure that takes into account what end users are experiencing when using VoIP services. The Mean Opinion Score (MOS) [4] is mainly used to measure QoE. The importance of QoE to VoIP service providers is vital. Poor QoE in VoIP services increase technical support budget, customers churn rate and lower revenue streams.

International Telecommunication Union (ITU) have pio-neered the investigation of VoIP quality in terms of QoE and proposed quantitative methods for measuring QoE [5]. However, the proposed methodology only considered network parameters such as packet loss and delay. It has been argued that it is not sufficient to measure QoE [6] by only considering

This research is supported by the EU FP7 GERYON project (contract No. 284863).

network parameters [7]. This argument triggered a broader definition of QoE which entails network parameters together with user related factors such as cognitive, psychological, behavioural and context in which VoIP services are consumed and created [6]. In this context, it is vital to consider users related factors, their devices, environment and network pa-rameters.

A scenario may arise whereby a user is on an important mobile VoIP video call session in which a battery life is not long enough to complete the session, moreover, the environment does not have facilities to charge the battery. If this situation is left without any action, then the battery life will greatly jeopardize the QoE. In this scenario, power conservation techniques have an important role to play.

Since CPU, LCD, GPS, Wi-Fi/3G and AV applications are well known to be the major power consumers, any effort to reduce power consumption of these mobile components will prove vital. In this scenario only GPS is not needed and is therefore switched off, LCD must not be switched off because of the importance of video display.

Popular power saving techniques for Wi-Fi/3G such as sleep scheduling cannot be applied in this scenario because of the real time communication nature. Thus, in this scenario, AV applications such video codecs are the main components to be adapted to conserve power. This approach leads to a power-driven VoIP quality adaptation scheme that aims to conserve power while keeping VoIP quality at an acceptable level.

As a proof of concept and to demonstrate the proposed scheme, an IMS based VoIP testbed is developed where two Android Developer 1 mobile handsets are used as IMS clients for VoIP calls registration, session initiation and termination over Wi-Fi access network. AMR and H264 codecs are used for voice and video communication, respectively. The use of AMR and H264 codecs enables VoIP quality adaptation by changing audio and video send bitrates (SBR).

Preliminary results have shown that the proposed scheme considerably extends the battery life and hence prolongs the communication while maintaining an acceptable VoIP quality. The rest of this paper is organized as follows. Section II outlines the most relevant related work. Section III presents the testbed and the experimental setup. Evaluation of power consumption of relevant mobile components is carried out in Section IV. The proposed scheme is outlined in Section V. Experimental results and evaluations are described in Section

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VI. Future work and conclusions follows in Section VII. II. RELATEDWORK

There exists a few power-driven VoIP quality adaptation schemes in the literature, the most recent and relevant one has been proposed by Csernai and Gulyas in [8]. A framework that reduces the power consumption of wireless mobile devices during streaming video content over Wireless LAN networks was proposed. The proposed framework optimized the overall power efficiency by controlling the sleep cycles of wireless network adapters based on video QoE. The framework used various SBR to adjust sleep intervals of the wireless adapter in order to maintain video quality while maximizing power efficiency.

The same approach was also investigated in [9] where the Power Save Mode (PSM) introduced in IEEE 802.11 standard [10] was used. Although the framework was claimed to save 20-30% of total system power but did not answer some key concerns of practical interests in the real world implementation of VoIP communication. A major concern in this framework is the accuracy of sleep cycles in the presence of important VoIP signalling traffic such as SIP, which is crucial for maintaining the state of the real time VoIP communication session without any delays.

Global adaptation framework for quality of experience of mobile services was proposed in [11], the framework aimed at efficiently detecting the changes of a mobile system status and carrying adaptations in a global manner in order to enhance QoE. Although the framework emphasized on monitoring bat-tery level but it was not specific on what adaptation actions to take if the battery level was low. Furthermore, the framework was not implemented for analysis and evaluation.

As argued in the case of the framework proposed in [8], under the presence of VoIP signalling traffic the sleep cycles technique is not realistic. The ideal technique is to reduce power consumption of the AV application while keeping the VoIP quality at an acceptable QoE level.

Therefore, this paper proposes power-driven VoIP quality adaptation scheme over wireless mobile devices in order to extend battery life and hence prolong VoIP communication session at an acceptable QoE level. The scheme periodically monitors the battery power and takes actions on what SBR to use in order to reduce power consumption of an AV application. The framework proposed in [8] showed that the higher the bitrates the more the power is consumed. This is because the power consumption of H.264/AVC depends on the quantization and entropy encoder parts which include, the computational complexity due to the number of pixels processed per unit time and coding level based computational complexity which results into several SBR [12].

III. EXPERIMENTALTESTBED

Figure 1 depicts the testbed developed to evaluate the proposed scheme. The testbed is based on an Open IMS Core for RTP session establishment and termination using Session Initiation Protocol (SIP) protocol.

WLAN Edge router

Fig. 1. Power-driven VoIP quality adaptation testbed

Two Android Developer Phone 1 (ADP1) mobile phones ported with IMSDroid [13] were used as IMS clients con-nected via an open source Netgear WGR614v8 Wi-Fi router. The VoIP voice and video call with AMR and H264 codecs were used (IMSDroid supports AMR-NB and H264 base profile 1, 2 and 3). Android power monitoring tool (Power-tutor) [14] was deployed into mobile phones to periodically monitor power levels of relevant mobile components, the data collection was done at an interval of one second. The mobile phone was stationery, situated at about 20m from the access point.

OpenSips [15] is used as an open source presence server and OpenXCAP [16] as an open source implementation of the XDM specification, for centralised storage of monitored battery life and power level consumption of relevant mobile components, the HTTP was used to upload these data at an interval of 5 seconds.

IV. POWERCONSUMPTIONEVALUATION

This section evaluates power consumption of relevant mo-bile components, the evaluated components have shown to significantly contribute to overall system power consumption. In this experiment, all data was collected and evaluated at one end (the callee phone) of the two mobile phones.

A. Wi-Fi Interface Power Usage

Figure 2 illustrates the power consumption of the Wi-Fi interface. The evaluation was done by recording the Wi-Fi interface power usage in the following steps,

Step 1: The Wi-Fi interface was off and as the result there

was no power consumption recorded.

Step 2: The Wi-Fi interface was enabled with no data

transmission, this depicts that an average of 38 mW was used by the Wi-Fi interface just for listening, this step is known as the low-power state.

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0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 900 1000 Power (mW) Time (Sec) --Step 1---Step 2---Step 3---Wifi Power Usage

Fig. 2. Wi-Fi power consumption

Step 3: The VoIP communication was on, this phase is

known as high-power state which according to [14], occurs when there is uplink and/or downlink data transmission.

The average power consumption at the high-power state recorded an average of 725 mW. Step 3 validates the Wi-Fi power consumption model outlined in [14] (c.f., (1) and (2)), which depends on the number of packets transmitted and received per second, uplink channel rate and uplink data rate. The uplink channel rate was at 54Mbps. The Wi-fi interface manufacturer is the Texas Instruments WL 1251B chipset and the CPU is MSM7201A chipset, including ARM11 application processor, ARM9 modem, and high-performance DSP.

βwif i= 710mW +βcr(Rchnl)×Rdata (1) and,

βcr(Rchnl) = 48−0.768×Rchnl (2) where, βwif i, Rchnl, Rdata and βcr are high-power state consumption of the Wi-Fi interface, Wi-Fi uplink channel rate, Wi-Fi uplink data rate and Wi-Fi power coefficient, respectively.

B. AV Application Power Usage

Figure 3 illustrates the power consumption of the AV application (IMSDroid). The evaluation followed the following steps

Step 1: The AV application was off and therefore no power

consumption was reported.

Step 2: The AV application was launched whereby the SIP

and RTP stacks were readied and VoIP registration process was done, an average of 230 mW power consumption was recorded. This step involved SIP signalling traffic exchange between the mobile phone and IMS entities, presence and xCAP servers.

Step 3: The voice only VoIP communication was then

established, this involved SIP signalling traffic for session negotiation which was followed by RTP voice media communication, this phase reported an average of 160 mW power consumption.

0 100 200 300 400 500 600 0 100 200 300 400 500 600 700 800 Power (mW) Time (Sec) ---Step 1----Step 2 ---Step 3---Step 4---Step 5---AV Power usage

Fig. 3. AV application power consumption

Step 4: The receive only video transmission, at this step

the power consumption jumped to an average of 370 mW.

Step 5: The send video button was pressed and the power

consumption increased to an average of 470 mW. C. LCD Power Usage

The power consumption of the LCD (TFT-LCD at glass touch-sensitive HVGA screen) remained constant at 533 mW as it was always on for the VoIP video communication display. The brightness level was 102.

D. Battery Discharge Rate

Figure 4 shows the battery state of discharge during the VoIP communication. It shows that the rate of battery discharge was higher at the transition from the low-power to high-power state. During the high-power state, the rate of battery discharge was at the steady state.

3.75e+06 3.8e+06 3.85e+06 3.9e+06 3.95e+06 4e+06 4.05e+06 4.1e+06 0 100 200 300 400 500 600 700 800 Voltage (mV) Time (Sec) Battery Discharge

Fig. 4. Battery state of discharge

E. Total Power Consumption

In Figure 5, the total power of the mobile phone is shown which includes all mobile components at each step,

Step 1: The Wi-fi interface is off

Step 2: The Wi-fi interface is on and on listening mode

Step 3: The AV application is launched

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0 200 400 600 800 1000 1200 1400 1600 1800 0 100 200 300 400 500 600 700 800 900 1000 Power (mW) Time (Sec) ---Step 1-- ---Step 2---Step 3 Step 4 Step 5 --Step 6---Step 7--- ---Step 8---Total power usage

Fig. 5. Total power consumption of the mobile phone

Step 5: VoIP session establishment is made

Step 6: RTP audio traffic communication

Step 7: Incoming video communication and

Step 8: Outgoing video communication

F. Uplink and Downlink Bytes

The downlink and uplink Bytes are demonstrated in Fig-ures 6 and 7, respectively, which clearly illustrate the traffic behaviour at each step during the VoIP communication session.

0 2000 4000 6000 8000 10000 0 100 200 300 400 500 600 700 800 900 Packets (Bytes) Time (Sec) ---Step 1----Step 2- -Step 3---Step 4---Step 5---Downlink Bytes

Fig. 6. Downlink Bytes

Figures 6 and 7, involved the following steps,

Step 1: The Wi-fi interface was off and therefore, no

downlink/uplink Bytes were recorded.

Step 2: The AV application was launched and the IMS

registration performed. This steps recorded an aver-age of 1100 and 900 Bytes for downlink and uplink, respectively. This is due to the SIP registration and presence signalling. The downlink had more activi-ties of downloading XML files for power monitoring at the time of registration.

Step 3: VoIP session establishment is established. This

step reported an average of 1400 Bytes due to SIP INVITE message flows.

Step 4: RTP audio traffic communication where an average

of 45 Bytes were observed for both downlink and uplink.

Step 5: Video communication where an average of 7500

Bytes were recorded for both downlink and uplink. The downlink video communication was started be-fore the uplink.

0 2000 4000 6000 8000 10000 0 100 200 300 400 500 600 700 800 900 1000 Packets (Bytes) Time (Sec) ---Step 1----Step 2--Step 3---Step 4---Step 5---Uplink Bytes

Fig. 7. Uplink Bytes

V. PROPOSED POWER-DRIVENVOIP QUALITY

ADAPTATIONSCHEME

By knowing power consumption of each component during VoIP communication and the state of battery discharge, it is important to identify which component to adapt in order to conserve power while maintaining acceptable VoIP QoE.

It is clearly observed in Figure 2 that Wi-Fi interface is the highest consumer of the overall power. One would argue that since its power consumption model is dependent on the transmitted and received data rate, adapting video and audio send bitrates could significantly reduce Wi-Fi interface power consumption, and as the result, battery life will be extended. But this is not the case for the Wi-Fi interface. As described in Section IV and proved in [14], there are two states of Wi-Fi interface power consumption, low-power state when there is no data being transmitted and/or received, and high-power state when there is data being transmitted and/or received. The two state of Wi-Fi power consumption are dependent on the number of packets transmitted/received per second. Authors in [14] proved that the high-power state is entered when number or transmitted/received packets per second is at least 8 packets/second.

The high-power state is noticed at the start of voice com-munication where the transmitted/received packets are at an average of 100 packets per second. This is because inter-departure and inter-arrival is at 20 ms per packet of the AMR Codec. The number of packets transmitted/received per second increased when the video communication was commenced, but this did not change the Wi-Fi power consumption. Figure 8 depicts the dependency of Wi-Fi power consumption states to the number of packets transmitted/received per second.

In this context, it is suggested to reduce power consumption of the AV application because codecs power consumption is dependent on the transmitted and/or received data rates as seen in Figure 3. Adapting SBR should be carried out while maintaining acceptable QoE.

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-100 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 900 1000 Time (Sec)

Number of packets transmitted/received Wi-Fi power usage (mW)

Fig. 8. Transmitted/recevied packets per second

As it can be seen in Figure 3, there is a significant difference in power consumption between audio and video codecs and hence the proposed scheme will only concentrate on adapting video SBR which also have wider range of variations than the former. From the empirical results, adapting audio SBR of AMR from 12.2 Kbps to 4.75 Kbps did not significantly save the power.

The proposed scheme makes use of the model proposed in [17] (c.f., Equation (3)) where the MOS value of at least 3.5 was proved to be an acceptable video QoE for slight movement video content type such as in VoIP video call. The proposed range of SBR between 100-500 Kbps provided maximum QoE of 4.2 at 15 frames per second when using QCIF video format. The SBR of 50 Kbps provided a good QoE of at least 3.5 MOS value. QCIF was specifically used in [17] because it was the recommended size for small handheld terminals which were the target applications of the research. However, the proposed scheme is not limited to QCIF and can be extended to higher resolutions.

M OS=a1+a2F R+a3ln(SBR)

1 +a4P ER+a5(P ER)2

(3) where, FR denotes the video frame rate, SBR presents the video send bitrates and PER is the packet error rates. The values of coefficients ai, i= 1. . .5 can be found in [17]. In this experiment P ER= 0.

Table I defines the battery charge levels (BCL) with the corresponding SBR values for VoIP quality adaptation.

TABLE I MAPPING OFBCLTOSBR Level BCL (%) SBR (Kbps) MOS 0 100-75 ≥500 4.2 1 75-50 300 4.2 2 50-40 100 3.5 2 40-15 50 3.5 3 15-0 only voice

The proposed scheme involves the following steps for which the algorithm is presented in Algorithm 1,

Step 1: Mobile phones upload their power capabilities

to the XDM server, i.e., maximum battery charge

capacity and the current battery charge level at the time of registration and then at an interval of 5 seconds provided mobile phones are still registered. 5 seconds is used as a standard from the well known RTCP communication.

Step 2: Mobile phones will use the presence server and

XDM server to retrieve power capabilities at the time of the VoIP session establishment.

Step 3: At the beginning of the VoIP session establishment

mobile phones will choose the right video SBR which corresponds to the acceptable QoE and battery charge level.

Step 4: Mobile phones will monitor power capabilities

through the published data in the XDM server at an interval of 5 seconds.

Step 5: From the battery charge level, mobile phones

will compute the state of the battery discharge and calculate the remaining time to reach 15% of the maximum battery capacity. The 15% is the threshold set for poor battery charge level.

Step 6: The remaining time to reach 15% of the maximum

battery charge capacity will determine the appropri-ate video SBR adaptation actions to be carried out while maintaining QoE at an acceptable level. Table I will be used to determine which SBR to use in the adaptation.

Step 7: If the poor battery charge level is reached then the

video transmission and the LCD will be switched off. Only the voice communication will be left to continue.

Algorithm 1 Power-driven VoIP Quality Adaptation Scheme

MBCC = Max Batt Charge Capacity() BCL = Batt Charge Level()

whilePhone is Still Registered to the IMSdo

forTime Interval of 5 Secondsdo

PUT Max Batt Charge Cap(MBCC,XDMS) PUT Batt Charge Level(BCL,XDMS)

whileVoIP Session is Ongoing do

BCL = GET Batt Charge Level(Callee,XDMS) MBCC = GET Max Batt Charge Cap(Callee,XDMS) Comput Batt DisCharge Rate(Callee,BCL)

Comput Remaining Time(BCL,MBCC,0.15) Adapt SBR If Needed()

ifBCL Threshold is reachedthen

Switch off video transmission Switch off LCD

end if end while end for end while

VI. EXPERIMENTALRESULTS

It can be seen that there is significant saving of power con-sumption when changing video SBR from 500Kbps to 50Kbps

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(c.f., Figure 9). A simple non-linear regression analysis is derived which gives a power consumption prediction model for the range of 50-500 Kbps SBR at 15 FR with the goodness of fit of 0.99 (c.f, Equation (4) ),

P ower= 95.211ln(SBR) + 311.84 (4)

where SBR(50Kbps ≤ SBR ≤ 500Kbps), is the video send bitrates of H264 codec.

300 320 340 360 380 400 420 440 460 480 50 100 150 200 250 300 350 400 450 500 Power (mW) SBR (Kbps)

Power usage at different SBR

Fig. 9. Power consumption at different SBR

The IMSDroid AV application shows noticeable power conservation (c.f., Figure 10) when changing from 500 Kbps to 50 Kbps. It can be seen from Equation (4) that the saving of power is at the range of 10-30% of the total system power.

0 100 200 300 400 500 600 0 100 200 300 400 500 600 700 800 Power (mW) Time (Sec) 500Kbps power usage 50Kbps power usage

Fig. 10. Power saving consumption at different SBR

VII. CONCLUSIONS ANDFUTUREWORK

This paper has proposed and evaluated the power-aware VoIP quality adaptation scheme over WLAN in mobile de-vices. The scheme uses video SBR for adaptation in order to conserve power while maintaining acceptable video QoE. Through preliminary results, the evaluation has shown that the proposed scheme can save power consumption between 10-30% of the total power consumption in Android Developer Phone 1.

The scheme can be extended to include several other video codecs, mobile devices and VoIP applications over various access networks such as UMTS and LTE. Future work will

also involve intelligent SBR adaptation scheme by using well known techniques such as Fuzzy logic. Subjective quality assessment with network parameters such as packet loss rate and delay will also be in the future work to validate the proposed scheme. The impact of mobility on the QoE will also be considered.

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[17] A. Khan, L. Sun, E. Jammeh, and E. Ifeachor, “Quality of experience-driven adaptation scheme for video applications over wireless networks,”

Figure

Figure 1 depicts the testbed developed to evaluate the proposed scheme. The testbed is based on an Open IMS Core for RTP session establishment and termination using Session Initiation Protocol (SIP) protocol.
Figure 3 illustrates the power consumption of the AV application (IMSDroid). The evaluation followed the following steps
Fig. 7. Uplink Bytes
Table I defines the battery charge levels (BCL) with the corresponding SBR values for VoIP quality adaptation.
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References

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