Battery Voltage Discharge Rate Prediction and Video Content Adaptation in Mobile

Abstract

According to Cisco， mobile multimedia services now account for more than half the total amount of Internet traffic. This trend is burdening mobile devices in terms of power consumption， and as a result， more effort is needed to devise a range of power-saving techniques. While most power-saving techniques are based on sleep scheduling of network interfaces， little has been done to devise multimedia content adaptation techniques. In this paper， we propose a multiple linear regression model that predicts the battery voltage discharge rate for several video send bit rates in a VoIP application. The battery voltage discharge rate needs to be accurately estimated in order to estimate battery life in critical VoIP contexts， such as emergency communication. In our proposed model， the range of video send bitrates is carefully chosen in order to maintain an acceptable VoIP quality of experience. From extensive profiling， the empirical results show that the model effectively saves power and prolongs real-time VoIP sessions when deployed in power-driven adaptation schemes.

Keywords

QoE； power； mobile devices； quality adaptation； discharge rate

1 Introduction

According to the Cisco Visual Networking Index of the Global Mobile Data Traffic Forecast， mobile video traffic accounted for 52% of total Internet traffic at the end of 2011 [1]. Mobile video traffic will continue to increase so that by 2016， two-thirds of mobile data traffic will be video. YouTube and Facebook account for more than 30% of all Internet traffic， YouTube accounting for a bigger portion of this traffic than Facebook [1]. Most video traffic goes to mobile devices； however， mobile applications use a considerable amount of power， especially when video is involved [2]， [3]. Processing power and storage capacity has grown exponentially over the past few years， but battery life has not. Power conservation in mobile devices has been extensively researched. Power-saving techniques， such as mobile resource management， power-aware operating systems， and Wi-Fi/3G sleep scheduling have been exploited [4].

CPU， LCD， GPS， Wi-Fi， and 3G components consume a lot of power， as do multimedia applications. Any effort to reduce power consumption in any one of these components and applications is of paramount importance. In the case of video VoIP， GPS can be switched off because it is not needed， and one of the network interfaces can also be switched off depending on the access network being used. However， the LCD must not be switched off in order to allow the video watching.

Sleep scheduling techniques cannot be applied in this situation because the communication is in real time. Sleeping schedules conflict with highly delay-sensitive VoIP services and degrade VoIP （QoE） [5]. Therefore， it is more appropriate to adapt multimedia VoIP applications than network interfaces in order to save energy. This approach has led to a content-adaptation technique that can be used to save energy and maintain VoIP QoE at an acceptable level [6].

This paper describes an extension of the power-driven adaptation scheme proposed in [6]. In this scheme， video send bitrates （SBRs） were mapped into battery charge levels. In the proposed scheme， battery life during VoIP communication was not predicted； hence， selection of SBRs did not determine the length of the VoIP session.

In this paper， we use a multiple linear regression analysis to predict the battery voltage discharge rate. By predicting this rate， we can accurately estimate the battery life， and appropriate SBRs can be used to save power or prolong the VoIP session. The secondary purpose of this paper is to revise the power-driven VoIP adaptation scheme proposed in [6] so that it includes the battery voltage discharge rate for SBR selection.

2 Related Work

The authors of [7] proposed a framework that reduces power consumption in Wi-Fi video streaming. The proposed framework uses various video SBRs to adjust sleep intervals of the Wi-Fi. This reduces power consumption and， at the same time， maintains video quality. In [8]， the same technique was also proposed， and the Power Save Mode （PSM） standard was used [9]. These frameworks do not address key issues in real-time VoIP communication， where the delay or loss of important signalling traffic， such as SIP， can affect the real-time communication.

The authors in [10] proposed a system context-aware approach to predict the life of a smartphone battery. They showed how changing mobile system components affects the life of a battery. A video player application with LCD brightness was used as a dependent variable； however， video content adaptation was not considered. This can also lead to another dependent variable， and more power can be saved. In this paper， we go beyond their approach and consider video content adaptation in terms of video SBRs.

In [6]， we proposed a power-driven adaptation scheme in which SBRs are switched in order to save power over Wi-Fi networks. Wi-Fi power is consumed in either the listening state or transmission state. Power consumption levels in these two states were constant and could be broken down into low and high power levels. However， we did not predict the battery voltage discharge rate， which is useful for estimating the battery life for each proposed SBR during the VoIP session.

Here， we propose a multiple linear regression model that can be used to predict the battery voltage discharge rate for several video SBRs. The proposed model is then used to estimate the battery life during a VoIP session. The range of video SBRs is carefully chosen in order to maintain acceptable QoE.

3 Experimental Testbed

We developed a testbed based on Open IMS Core to evaluate the proposed model （Fig. 1）. Session Initiation Protocol （SIP） [11]， the de facto signaling protocol in IMS， was used to establish and terminate a VoIP session.

Two HTC Dream G1 mobile phones with Android 2.3 were ported with IMSDroid [12] and used as clients for VoIP communication. The Universal Mobile Telecommunications System （UMTS） access network was provided by O2 UK. For voice sessions， the AMR-NB base profile codec was used， and for video sessions， the H264 base profile codec was used. An Android-based power monitoring tool called Powertutor [13] was installed on the mobile phones， and statistics were collected every second. The power sources for both mobile phones were rechargeable 1100 mAh， 3.7 V lithium-ion batteries.

OpenSips [14] and OpenXCAP [15] were deployed in the testbed for presence capabilities and to store monitored battery charge and power levels in a centralized way.

4 Evaluation of Power Consumption

In this section， we evaluate the power consumption of mobile components that significantly contribute to overall power consumption in the system. Power consumption differs between mobile devices because of software and hardware.

4.1 3G Interface

Fig. 2 shows the power consumed by the 3G interface in the HTC Dream G1. The chipset is a Qualcomm RTR6285. According to 3GPP， 3G has three states： IDLE， dedicated channel （DCH）， and forward-access channel （FATCH）. IDLE is the low-power-consumption state： the radio resource control is in IDLE when no data is being transmitted. DCH and FATCH are high-power-consumption states. DCH guarantees low delay and high throughput by reserving dedicated channels to mobile devices. The FATCH state occurs when there is less traffic， and the channel is shared between mobile devices.

Timers control the transitions between states. The transition from low-power to high-power state is immediate， but the transition from high-power to low-power state occurs only when the network has been inactive for a certain period of time. The transitional power from low-power to high-power state is called ramp power， and the transitional power from high-power to low-power state is called tail power [16]. The following steps describe how power is used by the 3G interface （Fig. 2）：

1） The 3G interface is off and no power is consumed.

2） The 3G interface is on， but no data is transmitted. An average of 10 mW is recorded， and the interface is in the low-power state.

3） VoIP multimedia transmission is started， and the interface is in a high-power state. An average of 570 mW of power is consumed. The power consumed during the transition from low- to high-power states is called ramp power.

4） VoIP multimedia transmission is stopped. The 3G interface waits for a fixed time before reverting to the low-power phase. An average of 401 mW of power is consumed， and the average wait time is six seconds. The power consumed during the transition from high- to low-power states is called tail power.

5） Low-power state.

The empirical results show that the high-power state of the 3G interface does not affect the transmission rate （Fig. 3）.

4.2 VoIP Application

Fig. 4 shows the power consumed by the IMSDroidapplication during the VoIP communication， which can be described by the following steps：

1） IMSDroid is off， and no power is consumed.

2） IMSDroid is switched on. The SIP and RTP stacks are initialized， and VoIP registration occurs at the IMS server. An average of 230 mW of power is consumed.

3） SIP signaling traffic for session negotiation is communicated which is then followed by the establishment of the RTP voice media communication. An average of 160 mW of power is consumed.

4） The incoming video transmission is initiated. An average of 370 mW of power is consumed.

5） The outgoing video is triggered， and power consumption increases to an average of 470 mW.

4.3 LCD

Fig. 5 shows the power consumed by the glass TFT-LCD touch-sensitive HVGA screen. At an average of 360 mW， power consumption is the lowest when the brightness level lowest （i.e. when the brightness level is 30）. At an average of 900 mW， power consumption is the highest when the brightness level is highest （i.e. when the brightness level is 255）. Lowering the brightness significantly saves power， but the brightness level must be carefully selected so that it does not degrade the VoIP QoE. We set the brightness level at 30， 127 and 255 in the proposed multiple-regression model.

4.4 CPU

Fig. 6 shows the power consumed by the MSM7201A chipset， which includes an ARM11 application processor， ARM9 modem， and high-performance DSP. At an average of 45 mW， power consumption is lowest when the VoIP application is not running. At an average of 335 mW， power consumption is highest when the VoIP application is running at 50 Kbps.

4.5 Total Power Consumption

Fig. 7 shows the total power consumed by the mobile phone during the video VoIP session， which can be described by the following steps：

1） The 3G interface is off.

2） The 3G interface is on and in listening mode.

3） The VoIP application is started.

4） The VoIP application is registered to the IMS.

5） The VoIP session is established.

6） RTP audio traffic flows.

7） The incoming video is played.

8） The outgoing video is transmitted.

5 Battery Voltage Discharge Rate Prediction

Model

The battery life can be estimated by predicting the battery voltage discharge rate during a VoIP session. It is given as

where β is the battery voltage discharge， and vi and vj （for

vi >v j ） are the battery voltages at time i and j.

Fig. 8 shows the battery voltage discharge rate for different SBRs during the VoIP session. In the steady state， power consumption and， consequently， battery voltage discharge increases as the video SBR increases [6]. An appropriate video SBR can be chosen to save power and/or prolong VoIP communication without compromising the video QoE. The range of H264 codec SBRs for which there is an acceptable QoE has been tabled in [17]. These SBRs were deployed in [6].

Through extensive profiling， we found that during the VoIP session， there is a linear relationship between battery voltage discharge （a dependent variable） and independent variables such as CPU， LCD， GPS， Wi-Fi， and 3G interfaces. The video SBRs can therefore be varied and multiple linear regression analysis used to predict the battery voltage discharge. The battery voltage discharge rate is the dependent variable， and the main power-consuming components （i.e. CPU， LCD， GPS， Wi-Fi， 3G and IMSDroid） are independent variables. During the VoIP session， the CPU， GPS， Wi-Fi and 3G are kept constant. The 3G and Wi-Fi interfaces are either on or off， and only one interface is used at a time during the VoIP session. In this paper， a 3G interface is used and is switched on， from the start to the end of the session. The brightness level of the LCD backlight in the HTC phones is allowed to vary between 30 and 255， which improves the quality of the video session. The GPS interface is not needed in this scenario and is therefore switched off. Table 1 lists independent variables and their range of values considered in this paper.

Table 2 shows the sample data for the battery voltage discharge， LCD， and VoIP application. The CPU frequency remained constant at 245 MHz during the course of the experiment. The 3G interface was switched on from the beginning to the end of the VoIP session and did not vary in its power consumption. The GPS interface was off and did not affect power consumption in the experiment.

Therefore， the remaining independent variables were the LCD and VoIP application via its video SBRs.

The regression model is then expressed as

where V is the battery voltage， X1 ， ... ， Xk are independent variables （Table 2）， and α and β1 ， ... ， βk are the regression coefficients to be estimated. The independent random error is given by ε. In regression analysis， α and β1 ， ... ， βk are estimated by assuming ε is normally distributed； that is， the mean μ = 0 and standard deviation δ = 1.

The method of least squares is used to calculate the coefficients of （2） in order to yield the best-fitting equation. In this paper， k = 2， X1 = VoIP， and X2 = LCD； therefore （2） becomes

If only VoIP is considered and the rest of the independent variables are kept constant， （3） can be reduced to

where β1 is the slope of the regression line that represents the battery voltage discharge rate contributed by the VoIP application， and α is the line intercept， which represents the voltage when the VoIP application is off. When the VoIP application is on， α is the combined voltage contributed by other independent variables.

6 Experimental Results and Evaluation

Experimental results in [6] showed that 10-30% power was saved when SBRs were changed from 200 Kbps to 50 Kbps （Fig. 9）. In this paper， the results [6] are extended and used to predict the battery voltage discharge in order to estimate the battery life. The estimated battery life is used as one of the inputs into the proposed power-driven adaptation scheme [6].

Fig. 10 shows the battery voltage discharge rate when the video VoIP session is operating at 50 Kbps. Fig. 11 shows the battery voltage discharge rate when the video VoIP session is operating at 200 Kbps. When the video VoIP application is running at 50 Kbps， the battery voltage discharge rate is 0.8635 mV/s. When the same video VoIP application is running at 200 Kbps， the battery voltage discharge rate is 1.104 mV/s. When the video VoIP application is running at 50 Kbps， the voltage drops to 2400 mV around 1800 s after the battery is fully charged. When the video VoIP application is running at 200 Kbps， the voltage drops to 2400 mV approximately 1500 s after the battery is fully charged.

With extensive profiling， we found that the CPU frequency was constant at 245 MHz during the VoIP session. When the 3G interface was always on， CPU frequency was constant. When the GPS was switched off， the remaining independent variables were LCD and VoIP. The multiple regression coefficients are estimated by using the least square method， and the following equation is derived：

From （5）， the intercept has not significantly changed （Figs. 10 and 11）. This behavior is expected because if the LCD backlight is switched off and the VoIP application is not running， the intercept is the expected maximum value when both LCD and VoIP application are switched off. The VoIP coefficient， which is the battery voltage discharge rate due to VoIP application， changes very little， and this means that power consumption of the VoIP application and LCD backlight is not related. The battery voltage discharge caused by the LCD backlight was 1.563 mV， and the battery voltage discharge caused by the VoIP application was 0.721 mV.

The proposed model is evaluated using the mean residual error and mean prediction error. The mean residual error was 0.7%， and the mean prediction error was 2.21%.

7 Power-Driven VoIP Adaptation Scheme

In [6]， a simple nonlinear regression analysis was done to estimate power consumption in the SBR range 50-500 Kbps （Fig. 12）：

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

The battery charge levels （BCL） were mapped to the corresponding SBR values for VoIP quality adaptation. This mapping was then used in the power-driven adaptation scheme （Table 3， Fig. 12）.

The BCL was not associated with the battery life； therefore， it was not possible to estimate how long it would take to stay in each BCL. The proposed battery voltage discharge rate model allows us to accurately estimate the battery life in each BCL. For example， when the VoIP application is running at 50 Kbps， the battery power drops to 2400 mV 1800 s after the battery is fully charged （Fig. 10）. The power-driven VoIP adaptation scheme in [6] is revised to include the battery voltage discharge rate model （Algorithm 1）：

1） The mobile phone uploads its maximum battery capacity， current battery charge level， and battery voltage discharge rate to the XDM server at the time of registration and then at five second intervals provided the mobile phone is still registered. Five seconds is specified in the RTCP communication standard.

2） The mobile phone uses the presence server and XDM server to retrieve power capabilities when initiating a VoIP session.

3） When initiating the VoIP session， the mobile phone chooses the video SBR for acceptable QoE and battery life.

4） The mobile phone monitors power capabilities and the battery life through the published data in the XDM server at five second intervals.

5） Using the battery charge level， the mobile phone computes the battery voltage discharge rate and calculates the battery life.

6） The remaining battery life determines how SBRs should be adapted while maintaining acceptable QoE.

7） If the battery is low， the video and LCD are switched off. Only voice communication is left running. In this paper， low battery charge is 1600 mV.

8 Conclusion and Future Work

In this paper， we have proposed a model for predicting battery voltage discharge rate in mobile devices on 3G networks. The model is based on a multiple linear regression analysis. We use a video VoIP application， in which different video send bit rates and LCD backlight levels are independent variables， and battery voltage is a dependent variable. The proposed model can be used to accurately estimate battery life in real time during critical VoIP communications， such as emergencies. The battery voltage discharge rate model has been used in the proposed power-driven adaptation scheme [6]， and it was found that 10-30% of the total power could be saved when video send bit rates were changed.

The proposed model can be extended to include several other video codecs， mobile devices， and VoIP applications.

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Manuscript received： January 31， 2013

Biographies

Is-Haka Mkwawa

Is-Haka Mkwawa （Is-Haka.Mkwawa@plymouth.ac.uk） received his PhD in computing from the University of Bradford. He is currently working as a research fellow on the EU FP7 GERYON project at Plymouth University. Since 2002， he has also worked in various capacities on the EU FP6 and FP7 projects at Plymouth University， the University of Bradford， and University College Dublin. He has previously worked on other projects， including ADAMANTIUM， VITAL， NoE Euro FGi， SFI， NoE Euro NGi， and IASON. He is the author of several published works on parallel computing and communication， distributed systems， next-generation networks， grid computing， VoIP quality adaptations， energy conservation techniques and mobility management in mobile and wireless networks， and performance analysis and evaluation of computer networks. He is the co-author of the textbook Guide to Voice and Video over IP： For Fixed and Mobile Networks.

Lingfen Sun

Lingfen Sun （L.Sun@plymouth.ac.uk） received her PhD degree in computing and communications from the University of Plymouth in 2004. She received her MSc in communications and electronics systems and BEng in telecommunications engineering from the Institute of Communications Engineering， China， in 1988 and 1985. She is currently a reader in multimedia communications and networks in the School of Computing and Mathematics， University of Plymouth. She has been involved in several funded projects， including FP7 GERYON （as scientific manager and principal investigator）， COST Action QUALINET， FP7 ADAMANTIUM， and FP6 BIOPATTERN. She also led an industry funded project on multimedia over 3G networks. She has published one textbook， four book chapters， and more than 60 peer-refereed technical papers. She was the chair of QoE Interest Group of IEEE MMTC during from 2010 to 2012. Her current research interests include multimedia quality assessment， QoE control and management， VoIP/IPTV， and multimedia services for emergency communications and eHealthcare.