MODELING POWER CONSUMPTION FOR VIDEO ... - IEEE Xplore

MODELING POWER CONSUMPTION FOR VIDEO DECODING ON MOBILE PLATFORM AND ITS APPLICATION TO POWER-RATE CONSTRAINED STREAMING Xin Li† , Zhan Ma‡ , Felix C. A. Fernandes‡ †

Dept. of Computer Science, George Mason University, Fairfax VA 22030 ‡ Dallas Technology Lab, Samsung Electronics, Richardson TX 75082

ABSTRACT This paper proposes an analytical power consumption model for H.264/AVC video decoding using hardware (HW) accelerator on popular mobile platforms. Our proposed model is expressed as the product of the power functions of video spatial resolution (i.e., frame size) and temporal resolution (i.e., frame rate). We have demonstrated that the same analytical model is applicable to different platforms. Model parameters are fixed for a specific platform. This indicates that HW accelerated video decoding is independent of the video content. Simulation results show the high accuracy for video decoding power prediction using proposed model, with the maximum relative prediction error less than 10%. Together with the video bit rate and perceptual quality models published in separated works, we propose to solve the power-rate optimized mobile video streaming problem, so as to maximum the video quality given the limited access network bandwidth and battery life for mobile devices. Index Terms— Power consumption modeling, video decoding, power-rate optimization mobile platform 1. INTRODUCTION Video applications, such as live streaming, video on demand, video recording as well as video conferencing are becoming increasingly popular on mobile devices (e.g., SmartPhone, tablet, etc). However, because of the tremendous energy consumption demands for video processing, power is an ever increasing concern in the design of a video codec on mobile platform. One motivation for power reduction is thermal management. A second key motivator for power aware video coding is improved user experience – namely, reduced power allows for longer battery life in a mobile device. Alternatively, power aware video coding allows for smaller batteries and handset form factors while maintaining the same playback duration as current technology. To propose the efficient power aware video processing, we must first understand the video processing power consumption on popular mobile platforms. There are several related works proposed in the literature [1, 2, 3], where [1, 3] discuss the software H.263 encoder Emails: [email protected], {zhan.ma, felix.f}@samsung.com

while [2] investigates MPEG-4 energy model on a ASIC implementation. All of them model the video encoding power (or energy) consumption at a fixed spatial and temporal resolution. In this paper, we model power consumption for video decoder. Specifically we model the hardware accelerated (HWA) video decoding power consumption on popular mobile platforms. We measure the H.264/AVC [4] baseline profile compliant video decoding power consumption 1 for prevailing video streaming applications on two popular SmartPhone models (e.g., Samsung Galaxy S and HTC EVO 4G), and develop the analytical model to relate the video decoding power consumption to the video spatial resolution (i.e., frame size), temporal resolution (i.e., frame rate) and amplitude resolution (i.e., peak-signal-noise-ratio [PSNR] which is usually controlled by the quantization). As an example for future advanced video streaming, we can estimate the video decoding power using proposed model, and decide the proper video stream resolutions at proxy considering the receiver’s remaining battery capacity, to prolong battery life. Although the model is built upon the H.264/AVC baseline profile, we verify that the same model is applicable to H.264/AVC high profile codec deployed in recent SmartPhones, such as Verizon Galaxy Nexus. We also verify that, within the acceptable prediction error, model parameters are fixed for a typical platform, indicating that HWA video decoding power is independent of the video content. We further apply proposed power consumption model, together with the video bit rate and perceptual quality models developed in separated works [5, 6], to do power-rate constrained mobile adaptive streaming to maximize the streamed video quality by jointly considering the limited access network bandwidth and battery power supply. Proposed models make the optimization problem analytical tractable, without requiring intensive online computing or off-line training process. To summarize, in this paper, we have the following contributions: • This is the first work developing an analytical model for 1 Please note that our power consumption measurement includes both computing logic and memory transfer.

HWA video decoding power consumption, where both computing logic and memory transfer power consumption are included into consideration. • We also propose to solve power-rate constrained adaptive mobile video streaming, which is necessary and useful in practice where both network bandwidth and battery power supply are constrained on mobile devices. To the best of our knowledge, there is no such prior work published in the literature. Proposed solution can be implemented under the adaptive HTTP streaming framework for practical adaptive mobile video streaming. The rest of this paper is organized as follows: Section 2 elaborates the analytical power consumption model for HWA video decoding on popular mobile platforms, while the methodologies for power measurement and experimental validation using various video contents are detailed in Section 3. We further discuss the potential model application in Section 4 for power-rate constrained adaptive mobile streaming and conclude the work in Section 5. 2. ANALYTICAL VIDEO DECODING POWER CONSUMPTION MODEL Theoretically, compressed video can be characterized by its spatial resolution s (i.e., frame size), temporal resolution t (i.e., frame rate) and amplitude resolution (i.e., SNR or quantization q). For instance, recent studies [5, 6] show that bit rate for compressed video can be modeled as the product of the power function of frame size s, frame rate t and quantization q, respectively. Meanwhile, perceptual quality of the compressed video can be expressed as the functions of the s, t and q as well. Intuitively, HWA video decoding power consumption could be hypothesized as a function of the s, t and q. For example, video stream with larger frame size and larger frame rate shall require more computing logic and definite more memory transfer, and result in more power consumption. Following the studies for video bit rate and perceptual quality modeling, we hypothesize the video decoding power consumption model as c s c t c q t q s , (1) P (s, t, q) = Pmax smax tmax qmin where Pmax = P (smax , qmin , tmax ) is the maximum power required for decoding the video at the maximum frame size smax , the maximum frame rate tmax and the minimal quantization qmin . smax , tmax and qmin are determined by the underlying HWA processing capability. For example, Samsung Galaxy S SmartPhone supports the video decoding up to level 3.1, i.e., bit rate 14 Mbps2 at 720p with 30 frame per second (30Hz). cs , ct and cq are model parameters. 2q

min can be estimated using maximum bitrate via many published rate model in literature.

3. VIDEO DECODING POWER MEASUREMENT AND MODEL VALIDATION This section discusses the decoding power measurement as well as the analytical model validation using various popular SmartPhones as listed in Table 1. Video Decoding P -

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Fig. 1. Illustration of video decoding power measurement

Table 1. Exemplified SmartPhones and Specifications Galaxy S HTC EVO 4G Galaxy Nexus Chipset Hummingbird Snapdragon OMAP4 Codec AVC baseline AVC high Memory 512M 1G 1G Display OLED LCD OLED 800x480 800x480 1280x720 Android 2.3.6 2.3.6 4.0.3

3.1. Power Consumption Measurement Power monitor [7] is used to characterize the SmartPhone battery, where instant power (at millisecond) can be recorded at connected computer (for later data post-processing). Figure 1 depicts the instrumental environment for video decoding power measurement. For each measurement session, we record the entire decoding power (for a video coded with typical s, q, t) Ptot (i), i = 0, 1, 2, . . . n − 1, where i is decoding frame index and n is the total frame number. To avoid the interference from other components on mobile platform, such as transceiver, we disable all unnecessary parts for video decoding. For every power measurement, it only includes the video decoding power and display (as well as the associated memory transfer during video decoding). Thus, for the i-th frame, video decoding power is the measured total power minus the display power, i.e., P (s, q, t)(i) = Ptot (i) − Pdisplay (i),

(2)

where Pdisplay (i) is the display power for rendering the i-th frame on the screen. For Samsung Galaxy S featuring the Super AMOLED (Active-matrix organic light-emitting diode)

where α = 1.02, β = 1.91 and γ = 2.43. R(x, y; i), G(x, y; i) and B(x, y; i) are linear RGB value for (x, y)-th pixel in i-th frame, x and y are bounded by the frame width and height. On the other hand, for HTC EVO 4G platform featuring the LCD panel, the display power is a constant at 250 mW for least backlight brightness, i.e., Pdisplay = 250 mW3 . By taking out the display power (either constant for LCD or frame content dependent for AMOLED), we can isolate the power consumption for video decoding component only, where both logic operations (i.e., computational clock cycles) and memory I/O are considered. Hence, the average power consumption for a video coded at typical s, q, t combination is 1 n−1 P (s, q, t)(i). (3) P (s, q, t) = i=0 n In the following context, we discuss the analytical model validation for different mobile platforms.

Predicted Ave. Power (mW)


Table 2. Video database and test spatial resolutions video spatial resolution (wxh) BQTerrace, stockholm 320x176, 416x240 , 576x320 oldtowncross, mobcal 640x352, 704x416, 720x480 intotree, shields , cactus 800x480, 864x512 1024x576 BasketballDrive 1152x640, 1280x720 BQMall, RaceHorses 416x240, 576x360, 800x480 city, crew, harbour, soccer 320x240, 416x360, 640x480 BasketballDrive

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Fig. 2. Illustration of decoding power estimation via (4) with decoded video resolution larger than WVGA (800×480) on Galaxy S. To cover the wide range of different s, q, t combinations, we have created video bitstreams using 14 video sequences 3 For LCD panel, the display power consumption is a linear function of the backlight brightness. In our simulation, we conduct the experiments with least brightness at indoor environment.


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panel, display power is related to the i-th video frame content (i.e., pixel R, G, B value), i.e., Pdisplay (i) = αR(x, y; i) + βG(x, y; i) + γB(x, y; i),

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Fig. 3. Illustration of decoding power estimation via (4) with decoded video resolution smaller than WVGA (800×480) on Galaxy S. from standard test video pool. Extensive simulations show that quantization doesn’t affect the decoding power for hardware codec solution. This is verified for all popular chip-sets listed in Table 1. Thus, (1) is reduced as the function of s and t, with parameter cq = 0, i.e., P (s, t) = Pmax

s smax

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(4)

Table 2 details the video sequences and corresponding spatial resolutions. Temporal resolutions are fixed with 7.5, 15 and 30 Hz. As aforementioned, compressed videos are compliant with the H.264/AVC baseline profile (less than level 3.1) using x264 [8]. To save the space, we present the model accuracy for videos with the maximum spatial resolution at 720p. Other videos have the similar high accuracy. Table 3. Power consumption model parameters and emax (decoded video resolution larger than WVGA) for Galaxy S video cs ct Pmax emax BasketballDrive 0.3950 0.2650 370 7.23% BQTerrace 0.4180 0.2510 363 5.72% cactus 0.4030 0.2330 350 7.96% oldtowncross 0.4050 0.2420 355 6.22% mobcal 0.4150 0.2510 374 7.57% intotree 0.4040 0.2700 372 6.17% shields 0.3890 0.2440 368 5.55% stockholm 0.3930 0.2480 359 6.16% ave. 0.4027 0.2505 364 6.58% For both Galaxy S and EVO 4G, the maximum screen resolution is WVGA (800x480). For streams which have decoded resolutions smaller than WVGA, video will be automatically up-scaled to fit the screen size. Similarly, for streams with decoded resolution larger than WVGA, decoded videos are down-scaled to WVGA. We are using the default down-scaling and up-scaling rendering algorithm without any change. As shown in later simulation result, the same analyt-

Table 4. Power consumption model parameters and emax (decoded video resolution smaller than WVGA) for Galaxy S video cs ct Pmax emax BasketballDrive 0.3240 0.1090 288 5.26% BQTerrace 0.3400 0.1110 284 6.37% cactus 0.3260 0.0820 278 4.71% oldtowncross 0.3300 0.1130 284 6.64% mobcal 0.3070 0.0930 293 6.12% intotree 0.3370 0.1370 292 6.75% shields 0.3160 0.0970 292 5.03% stockholm 0.3190 0.1010 285 5.81% ave. 0.3249 0.1054 287 5.84%



ical power consumption model is applicable for both cases, but model parameter values differ. Fig. 2 and 3 show the predicted and actual measured video decoding power consumption for Galaxy S, with model parameters and maximum relative prediction error emax listed in Table 3 and 4. Here, we define emax = max(|Ppred − Pactual |/Pactual ) with Ppred and Pactual indicating the model predicted and actual profiled power consumption. Similarly, Fig. 4 and Table 5 illustrate the decoding power prediction accuracy using proposed model for HTC EVO 4G. Upper part of Table 5 is dedicated for the decoded video resolution larger than mobile screen while the bottom part is for video resolution smaller than mobile screen. As we can see, proposed model can estimate the decoding power very well. We also verify that the same model works for H.264/AVC high profile decoding on Galaxy Nexus. However, because of the space limitation, the result is not included. HTCBasketballDrive 1000

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Fig. 4. Illustration of decoding power estimation via (4) on EVO 4G (first row: decoded video resolution larger than WVGA; second row: decoded video resolution smaller than WVGA).

3.2. Model Error Robustness Calibration Using Fixed Model Parameter As aforementioned, our proposed model is applicable to different platforms, but model parameter values differ. For the

same platform, because of the mobile screen size limitation (up to WVGA), video decoding power model has quite different model parameter values for decoded video with resolution larger or smaller than WVGA (as shown in Table 3 – 6). But for each case, the parameters are quite similar. Hence, we conduct the model error robustness calibration to demonstrate the prediction error by using fixed cs , ct and Pmax . We simply calculate the mean of Pmax , cs and ct in Table 3 – 6, and plug into (4) to find out emax . Results show that emax is also less than 10%4 on average for all cases, which leads to the reasonable conclusion that HWA video decoding power consumption is platform dependent, but independent of the video content. With fixed model parameters, we can reduce the power-aware system design complexity significantly. In the following section, we will use the fixed parameters for power model. Table 5. Power consumption model parameters and emax for EVO 4G (decoded video resolution larger than WVGA) video cs ct Pmax emax BasketballDrive 0.4530 0.3410 923 8.64% cactus 0.4440 0.3320 885 8.71% intotree 0.4300 0.3430 869 8.14% oldtowncross 0.4520 0.3370 849 8.50% ave. 0.4447 0.3383 882 8.50%

4. POWER-RATE OPTIMIZED ADAPTIVE MOBILE VIDEO STREAMING There are several ways realizing the video streaming in practice. Among them, HTTP streaming is one of the most popular implementation. For example, Youtube, Hulu and etc adopt such implementation for their video streaming service. To overcome the heterogenous networks and clients, there are multiple copies of the same content stored in the server which are encoded into different spatial resolutions (noted as Quality by Youtube), and sliced into segments in time-aligned fashion. Users can choose a certain version (such as 480p) at the beginning, and are able to switch to the 720p or 360p during 4 Overall, the error is less than 3% assuming the video decoding power consumes 30% of the total system power.

Table 6. Power consumption model parameters and emax for EVO 4G (decoded video resolution smaller than WVGA) video cs ct Pmax emax BasketballDrive 0.3580 0.1450 604 9.11% cactus 0.3630 0.1430 586 6.47% intotree 0.3580 0.1480 574 8.16% oldtowncross 0.3690 0.1400 567 8.71% ave. 0.3620 0.1440 583 8.11%

the streaming session manually, simply speaking, depending on the network bandwidth. Instead of manually switching, adaptive HTTP streaming can switch the content segments automatically to meet the network bandwidth variation. In addition to network variation, battery life is becoming another crucial concern for video streaming on mobile devices. On the other hand, besides the Youtube’s solution where the same video content is stored with several versions at different spatial resolutions, we propose to extend such deployment to allow more flexibility, where the same content can be encoded at different combinations of spatial resolution (i.e., frame size s), temporal resolution (i.e., frame rate t) and quantization (i.e., q as bit rate control), and sliced into time-aligned segments. Therefore, client can request the best stream adaptively according to the network condition and its battery status. This problem is generally recognized as the power-rate optimized video streaming, where the solution requires the accurate models for video decoding power consumption (as shown in above sections), video bit rate and video perceptual quality, with respect to the frame size s, frame rate t and quantization q.

of frame size, frame rate and quantization. As an example, Tables 7, 8 show the model parameters and accuracy for “city”, “crew”, “harbour”, “ice” and “soccer”. These sequences are coded into 3 spatial resolutions, including 4CIF, CIF and QCIF, 4 temporal resolutions, including 30, 15, 7.5 and 3.75 Hz, and 5 amplitude resolutions at QP 28, 32, 36, 40 and 44 (to cover popular bit rate range for networked video). Table 7. Rate Model Parameter and Its Accuracy aq as at Rmax PC

city 1.371 1.047 0.233 1512 0.9995

crew 1.095 0.785 0.471 2429 0.9993

harbour 1.248 0.894 0.397 3818 0.9977

ice 0.86 0.667 0.438 975 0.9992

soccer 1.086 0.88 0.39 2268 0.9995

ave. 1.132 0.855 0.386 2201 0.9990

Table 8. Quality Model Parameter and Its Accuracy bq ˆbs bt PC

city 7.25 3.52 4.10 0.998

crew 4.51 4.07 3.09 0.996

harbour 9.65 4.58 2.83 0.992

ice 5.61 3.68 3.00 0.993

soccer 6.31 4.55 2.23 0.992

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4.1. Analytical Rate and Perceptual Quality Models Recent studies show that video rate and perceptual quality models at different combinations of frame size, frame rate and quantization can be well captured by R(q, s, t) = −aq a s a t q s t Rmax , qmin smax tmax

(5)

Q(q, s, t) = qmin

1 − e−bq q 1 − e−bq

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t

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1 − e−bt ( tmax ) , (6) 1 − e−bt

where qmin , smax and tmax should be chosen according to the underlying application, Rmax is the actual rate when coding a video at qmin , smax and tmax . βs = 0.74, βt = 0.63, bs = ˜bs (ν1 QP + ν2 ) when QP5 ≥ 28 and bs = ˜bs (28ν1 + ν2 ) when QP < 28, with ν1 = −0.037, ν2 = 2.25. The ratemodel parameters, Rmax , as , at , aq , and quality model parameters bq , ˆbs and bt control how fast the rate and quality, respectively, drop as the spatial, temporal, or amplitude resolution decreases. These parameters depend on the the motion and texture characteristics of the underlying video, and can be estimated from certain features computable from original video. Because of the space limitation, we will not include the parameter prediction in this paper. With only a few content dependent parameters, the proposed rate and quality models are highly accurate, with average Pearson correlation (PC) coefficient greater than 0.99 over all test video sequences coded at multiple combinations 5 In

H.264, QP = 4 + 6 log2 (q).

4.2. Power-Rate Optimized Adaptive Video Streaming Power-rate optimized adaptive video streaming can be recognized as Determine s, t, q, max Q(q, s, t), Subject to R(q, s, t) ≤ R0 , P (q, s, t) ≤ P0 ,

(7)

where R0 and P0 are the constraints. We choose to use the video decoding power consumption model for Galaxy S platform, i.e., Pmax = 287mW, cs = 0.3249 and ct = 0.1054. The same methodology can be applied to other platforms as well. It is difficult to derive the closed form of Qopt , qopt , sopt and topt , with respect to the bit rate R and power consumption P . We choose to solve the problem numerically for continuous q, s and t, and further assume the power consumption constraint is discrete. Given that Pmax = 287 mW, three power consumption levels, i.e., 300mW, 200mW and 100mW, are selected to experiment three different scenarios, where 300mW indicates the sufficient battery power, 200mW and 100mW stand for intermediate and low battery power, respectively. Fig. 5 shows the Qopt , and corresponding normalized qopt , normalized sopt , normalized topt versus bit rate R, under three given power consumption levels. From the plots, we can see that the video quality degrades when there is not sufficient battery power, for instance from 300mW to 200mW, and is even worse when the power supply is 100 mW. Note that the rates at which s jumps or t jumps are sequence dependent. For sequences with high texture details (e.g., city, harbour),

we see that s jumps to the highest level earlier. For sequences with high motion (e.g., soccer), t jumps to the highest level earlier.

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decoding on popular Samsung Galaxy S and HTC EVO 4G. It is also verified that the same model works for the H.264/AVC high profile video decoding on recent Galaxy Nexus platform. We first show that the decoding power is independent of the quantization (or bit rate). For different spatial and temporal combinations, simulation results show that our proposed analytical model can accurately estimate the video decoding power, with the maximum relative error less than 10%. Additional error robustness calibration shows that we can apply the fixed cs , ct and Pmax with prediction error less than 10% as well. Together with the published video bit rate and perceptual quality models, we propose to solve the power-rate optimization to provide the best video quality. It is a practical problem for mobile video streaming, where both access network bandwidth and battery power are constrained. In practice, such power-rate optimization can be implemented under the adaptive HTTP streaming framework to provide the best video streaming quality in response to the limited network bandwidth and battery power supply from mobile subscribers.

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Fig. 5. Qopt , normalized qopt , normalized sopt and normalized topt versus bit rate at different power level It is not practical to have continuous q, s and t in reality. Therefore, we also show the experiments assuming the discrete q, s and t. More specifically, we choose the QPs equal to 28, 32, 36, 40, and 44 with corresponding q ∈ [16, 26, 40, 64, 104], t ∈ [3.75, 7.5, 15, 30] Hz and s ∈ [QCIF, CIF, 4CIF]. Overall, we have 60 different combinations of q, s, t, and its associated Q(q, s, t), R(q, s, t) and P (q, s, t). We can derive the analytical approximation assuming the continuous q, s, t, and then quantize the value to the feasible discrete combinations. Alternatively, because of the limited number of combinations in practice, we can also transverse all possible q, s, t and pick the one that yields the maximum quality, given the bit rate and power consumption constraints. In video streaming scenario, searching the best combination of q, s and t (i.e., corresponding to the best video quality) is placed at powerful server in response to the feedback from client about its network bandwidth and battery power. Server will switch to stream the proper video segments corresponding to the best combination of q, s, t based on the decision. 5. DISCUSSION AND CONCLUSION This paper presents the analytical power consumption model for hardware accelerated H.264/AVC baseline profile video

6. REFERENCES [1] X. Lu, T. Fernaine, and Y. Wang, “Modelling power consumption of a H.263 video encoder,” in Proc. of IEEE ISCAS, May 2004. [2] T. Gan, K. Denolf, G. Lafruit, I. Moccagatta, A. Dejonghe, and G. Lenoir, “Modelling Energy Consumption of an ASIC MPEG-4 Simple Profile Encoder,” in Proc. of IEEE ICME, July 2007. [3] Z. He, Y. Liang, L. Chen, I. Ahmad, and D. Wu, “Powerratedistortion analysis for wireless video communication under energy constraints,” IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 1, pp. 31–42, Jan. 2006. [4] H.264/AVC, Draft ITU-T Rec. and Final Draft Intl. Std. of Joint Video Spec. (ITU-T Rec. H.264\ISO/IEC 1449610 AVC), Joint Video Team, Doc. JVT-G050, Mar. 2003. [5] Z. Ma, M. Xu, Y.-F. Ou, and Y. Wang, “Modeling Rate and Perceptual Quality of Video as Functions of Quantization and Frame Rate and Its Applications,” IEEE Trans. Circuit and System for Video Technology, vol. 22, no. 5, pp. 671–682, May 2012. [6] Y.-F. Ou, Y. Xue, Z. Ma, and Y. Wang, “A perceptual video quality model for mobile platform considering impact of spatial, temporal, and amplitude resolutions,” in Proc. of IEEE IVMSP, Ithca, NY, June 2011. [7] “http://www.msoon.com/labequipment/powermonitor/,” 2010. [8] “http://www.videolan.org/developers/x264.html,” 2010.