Mobile cloud gaming allows gamers to play games on resource-constrained
mobile devices, and a measurement study to quantity the client
performance and energy consumption is crucial to attract and retain
gamers. In this paper, we adopt an open source cloud gaming platform,
GamingAnywhere, to conduct extensive experiments on real mobile devices.
Our experiment results show several insights that are of
interests to researchers, developers, and gamers. First, compared to
native games, mobile cloud games save energy by up to 30%. Second, the
hardware video coders achieve higher frame rates but suffer from a small
unnecessary buffering delay, and thus is less ideal for fast-paced
games. Third, the frame rate, bit rate, and resolution all affect the
consumption, while frame rate imposes the highest impact. Last, cellular
networks incur 30%-45% more energy consumption than WiFi networks,
and the event processing of touch screens is also energy-hungry. These
findings shed some light on further enhancements of the emerging mobile
cloud gaming platforms.
Cloud gaming service providers
offer on-demand cloud games to users. These games normally run on
powerful cloud servers, and the game scenes are captured, encoded, and
streamed to thin clients running on desktops, laptops, and TV set-top
In contrast to the aforementioned devices, mobile devices, such as
tablets and smartphones, have limited computation power and are
battery-powered. Therefore, running cloud gaming clients on these
resource-constrained devices may lead to inferior performance and high
energy consumption. For example, the gaming frame rate may become too
low for smooth game play due to insufficient CPU power for video
decoding operations. This results in degraded gaming quality and may
drive gamers away. On the other hand, when gamers play cloud games, the
communication, computing, and display components on mobile devices all
consume nontrivial energy, which may quickly drain the battery and
prevent gamers from using their mobile devices for other purposes,
including making phone calls. Hence, we believe that carefully measuring
the performance and energy consumption of mobile clients would be
critical to the success of the mobile cloud gaming ecosystem.
In this paper1, we adopt an open source cloud gaming platform, GamingAnywhere
(GA) [9,10], to setup a real mobile cloud gaming testbed.
We conduct extensive experiments on the testbed to answer the following
Does running cloud games save energy compared to native
mobile games? While in first glance, offloading games to cloud servers
saves significant amount of computation energy, doing so however also
increases energy consumption in network communication. This is partially
due to the nature of real-time continuous video streams incurred by
cloud games, which prohibits the wireless interfaces from being turned
off during the course of game play. Hence, we need a carefully designed
measurement study to understand the energy consumptions corresponding to
the two approaches.
What are the pros and cons of the hardware video decoder?
The software video decoder, if used, may incur significant CPU workload
on mobile clients, which in turn slows down the overall frame rendering
while consuming significant energy. One possible solution is to leverage
the hardware video decoders built in modern mobile devices. However,
hardware video decoders are usually less configurable than the software
ones. Thus, detailed empirical comparisons may shed light to the
tradeoff between the hardware and software video decoders.
How does the server configuration affect the client
performance? While there are a number of configurable parameters that
affect the offered cloud gaming quality, selecting the best
configuration for a particular game session at any time is a challenging
task. Thus, a comprehensive set of experiments is required to thoroughly
quantify the impact of each parameter, such as frame rate and
resolution, on the client performance and energy consumption. The
measurement results are useful to researcher, developers, and gamers to
better configure their cloud gaming servers.
Does the mobile client suffer from any power-hungry
components other than video decoders? Although the software video
decoders incur the majority of the CPU workload, which consumes much
energy, we also need to identify other components, if any, that consume
nontrivial energy. This is because, the battery technology is not
improved as fast as other hardware components , and
low-power (i.e., sustainable) mobile gaming is crucial to retain the
Our extensive experiments and in-depth analysis reveal several insights
that lead to design recommendations for future developments of mobile
cloud gaming platforms. To the best of our knowledge, this is the first
of its kind in the literature.
2 Related Work
Various optimization approaches have been proposed for mobile cloud gaming
platforms, which can be classified into: specialized video
codecs [6,16,3] and system
adaptations [1,17,14], Specialized video codecs leverage the
properties of computer rendered graphics to reduce the downlink bandwidth
consumption of mobile cloud games. In particular, Hemmati et al. 
selectively encode game objects to save network bandwidth and rendering power
while maintaining gaming quality. Shu et al.  adopt 3D warping for
light-weight post-rendering manipulation on mobile clients, in order to reduce
the network bandwidth and cope with network delay. Chuah and
Cheung  render low-quality game scenes on mobile devices while
streaming the difference between low- and full-quality game scenes from cloud
servers, so as to trade client computation complexity for communication
complexity. System adaptations dynamically adjust the system resources across
multiple distributed servers for better overall performance. Cai et
al.  divide computer games into small components, and dynamically
move these components across multiple distributed servers to meet the demands
from mobile gamers. Wang and Dey  adjust the visual effect levels of
computer games to trade off the server computation load and user-perceived
quality. Liu et al.  present a subjective model to approximate the
user experience under diverse video contents, coding parameters, and network
conditions, in order to guide their adaptive rendering component for better
gaming quality. These optimization
techniques [6,16,3,1,17,14] are complementary to
the measurement studies presented in this paper.
Both objective measurement studies [15,2] and subjective user
studies [12,11] on cloud gaming using desktops have been done in
the literature. We focus on mobile cloud gaming, which has only been recently
considered [13,10,5]. Lampe et al.  adopt three
performance metrics: latency, energy, and cost, trying to demonstrate the
feasibility of mobile cloud gaming. In our earlier work , we
conduct a user study to quantify the impact of different configurations on
mobile gamer satisfaction. In contrast, the current paper concentrates on the
objective measurements on mobile devices. Hans et al.  measure
the energy consumption of Android smartphones running a cloud gaming client,
and is probably the closest work to ours. While their findings on energy
consumption are in-line with ours, Hans et al.  is different from
the current paper for two main reasons: (i) we measure both client performance
and energy consumption, while they focus on energy, and (ii) we deploy several
actual games in our cloud gaming platform, while their CPU/GPU workloads are
The current paper is built upon our earlier work on developing
GamingAnywhere, an open source cloud gaming platform [9,7], and
porting the cloud gaming client to mobile devices . The
proposed mobile cloud gaming platform is transparent to existing PC
games, and is of interest to researchers and developers for experiments
and further enhancements.
Figure 1: The GA experiment testbed used throughout this paper.
3.1 Environment Setup
Figure 1 shows the testbed used in our experiments. The
testbed consists of a server and a mobile client connected via a
wireless access (a campus WiFi or a 3G cellular network). We install
five games of different genres on the GamingAnywhere (GA) server: Super
Smash Bros, Limbo, Batman, Mario Kart, and Zelda. Super Smash Bros is a
fighting game, Limbo is a 2D scrolled adventure game, Batman is a 3D
adventure game, Mario Kart is a 3D racing game, and Zelda is an
role-playing game. We study the GA mobile client's performance and
energy consumption using these five games, and report the sample results
from Super Smash Bros if not otherwise specified. To compare cloud and
native games, we adopt a cross-platform OpenGL game: GLTron, which runs
on both the server and Android devices. GLTron is a 3D snake-like game.
The server has an Intel Q6600 2.4 Ghz quad-core CPU and runs Windows 7. We
consider two mobile devices: an ASUS Nexus 7 tablet and a Sony Xperia Z
smartphone. The tablet has a Nvidia Tegra 3 1.2 quad-core process with 1 GB ram, and
the smartphone has a Qualcomm Snapdragon APQ8064 1.5 GHz quad-core processor wth 2 GB ram. Both mobile
devices run Android 4.4.2.
We adopt two tools, UseMon and Current Widget, to collect
measured CPU utilization and power consumption of a mobile device,
During the experiments, we set the screen brightness to medium, and always keep
the battery level above 70% to avoid noises due to battery's nonlinear
discharging characteristics (details are given in
3.2 Controlled Parameters
Table 1 lists the controlled parameters during the experiments.
First our mobile client supports both software and hardware video decoders. The
software decoder is provided by the ffmpeg project, and the hardware
decoder is accessed via Android's MediaCodec framework. We use the
popular H.264 coding standard, which is supported by
all the chosen implementations. Second, we selectively disable and
enable the controller on mobile devices, which is a transparent overlay
over the video surface. When the controller is disabled, we play the
games on the server. This is to isolate the additional energy
consumption due to: (i) activating touch screens and (ii) handling the
user input events. The remaining three parameters, resolution, bitrate,
and frame rate, are for video codecs. In each experiment, we fix two
video codec parameters, and vary the other one. We let 640x480, 30 fps
(frame per second), and 3 Mbps be the default settings, if not otherwise
specified. The goal is to quantify the impacts of different parameters
on client performance and energy consumptions.
Table 1: Controlled parameters
Video codec parameter‡
640x480, 960x720, 1280x720
1 Mbps, 3 Mbps, 5 Mbps
10 fps, 30 fps, 50 fps
‡ Default values are highlighted in boldface.
3.3 Baseline Energy Measurement
We measure the baseline energy consumptions before conducting the
We close all irrelevant applications and services, turn on the display,
and set brightness to medium.
We find that the CPU utilization is close to zero. We measure the current and
voltage for each mobile device, sampled at 1 Hz.
The results are shown in Figure 2.
On both devices, we observe that when the battery level reduces,
the voltage gets lower and the current gets higher.
When the battery level is lower than 60%, the current
exceeds the average. For fair comparisons, we only conduct experiments when
battery level is higher than 70%.
Based on the measurements, the baseline power consumption for Nexus 7
and Xperia Z are 1.7 W and 1.1 W, respectively.
Figure 2: The voltage and current levels measured under the baseline configuration.
4 Measurement Results
4.1 Software vs. Hardware Video Decoders
Table 2 shows the frame rates achieved by the
software decoders. This table reveals that while software decoders work
well when the resolution, frame rate, and bitrate are low, they fail to
achieve the configured frame rate when these video codec parameters are
high. This can be attributed to the limited CPU resources on the mobile
devices. We then switch to the hardware decoders, which run faster but
do not report the achieved frame rate. We observe fairly constant frame
rate under different video codec parameters. We make an interesting
observation: the hardware decoders on both mobile devices buffer 1 or 2
decoded frames, which lead to unnecessary delay. Such limitation renders
the hardware decoders less suitable to mobile cloud gaming platforms
with longer network latency and fast-pace games.
Next, we zoom into two video codec configurations: (i) light, with
640x480, 10 fps, and 3 Mbps and (ii) heavy, with 1280x720, 30 fps,
and 3 Mbps. We run each experiment for 15 minutes, and plot the
cumulative distribution function (CDF) curves of per-core CPU
utilization in Figure 3, where N7 and XZ represent
the tablet and smartphone respectively. We first observe that, for each
mobile device, the two curves (light and heavy) of the hardware codecs
are very close. This validates the aforementioned observation: the
hardware decoders achieve the same frame rate under different video
codec parameters. In contrast, for each mobile device, the gap between
two curves of the software codec is much larger. Last, our measurements
on the power consumption leads to similar observation (figure not shown
due to the space limitations). The hardware decoders consume power
levels that are about two times of the baseline, which is independent to
video codec parameters. In contrast, the software decoders are sensitive
to video codec parameters, and draw power consumptions that are between
two and three times of the baseline.
Table 2: Achieved decoder frame rate (in fps)
1280x720, 5 Mbps
1280x720, 3 Mbps
1280x720, 1 Mbps
960x720, 5 Mbps
960x720, 3 Mbps
960x720, 1 Mbps
640x480, 5 Mbps
640x480, 3 Mbps
640x480, 1 Mbps
Figure 3: Comparison of CPU utilization with the hardware and
software decoders under different workloads.
4.2 Video Codec Parameters
Figure 4: Measured CPU utilization and power consumption under various codec parameters.
We next present the CPU utilization and power consumption under
different video codec parameters. We repeat the 3-minute experiment by 5
times, collect samples at 1 Hz, and depict the average results with
minimum and maximum in Figure 4. This figure reports
average CPU utilization, i.e., 25% CPU utilization is equivalent to a
fully-loaded CPU core. We make two observations: (i) higher bitrates,
frame rates, and resolutions consume more resources and (ii) the
software decoders consume more resources. Next, we take a closer look
at how each codec parameter affects the performance of the hardware
decoders. We do not consider the software decoders, because neither of
the considered mobile devices can keep up with the high frame rate. We
define the parameter impact factor as follows. Given a parameter p and
a function fp that quantifies the load of p based on its
parameters. Suppose p is altered from ci to cj, we write the
increased load Lp as
Lp = [(fp(cj)−fp(ci))/(fp(ci))].
We also measure the battery level differences mi and mj for ci and
The increased overhead Op for p is defined as
Op = [(mj−mi)/(mi)].
Last, the impact factor for parameter p is written as
[(Op)/(Lp)]. Note that we measure the battery level difference to
define the parameter impact factor because CPU utilization does not
fully reflect system loads, as some workload is offloaded to the
hardware decoders. Table 3 lists the impact factors of the
parameters in hardware decoders. This table shows that the frame rate
has the highest impact, and the resolution has the lowest.
Table 3: The impact factors of hardware decoder parameters
† a, b, and c are the minimal, median,
and maximal values of each parameter.
4.3 Game Genres
We report the CPU utilization and energy consumption of different game
genres in Figure 5. All the games are configured to stream
game scenes at 1280x720, although only Windows games (Limbo, Batman, and
GLTron) support 1280x720: the game scenes captured from N64 emulator
(Super Smash Bros, Mario Kart, and Zelda) have to be up-sampled to
1280x720 before being streamed. Therefore, the game scenes from the N64
emulated games contain less details, which in turn consume less
resources. The power consumption fluctuations caused by all game genres
are within ±5%. If we separate the Windows and N64 games, the
fluctuations are about ±2%. We conclude that game genre has very
little impact on cloud gaming CPU utilization and power consumption.
Figure 5: CPU utilization and power consumption for different game genres.
4.4 Cloud versus Native Games
Next, we play Super Smash Bros and GLTron as cloud games and GLTron as a
native game. Cloud games are configured to stream at 1280x720
The results are shown in Figure 6.
In the figure, "Cloud#1" and "Cloud#2" correspond to Super Smash Bros and
"Native" is the Android version of GLTron.
It is clear that the native game consumes much more resources than cloud games:
the CPU consumption is doubled and the power consumption is also increased by more than
30%. We emphasize that GLTron is not very visually-rich, but
running it natively incurs nontrivial resource consumption. The resource
consumption gap between the cloud and native games will be even larger for modern
3D games. Last, we make another observation: enabling the controller
results in additional resource consumption. We take a deeper look at this
Figure 6: CPU utilization and power consumption for
cloud and native games.
4.5 Other Components
We study the impact of other components on resource consumption,
including the wireless access links and touch screens.
Figure 7 shows the resource consumptions by using
different wireless access links. We only report results from Xperia Z
because Nexus 7 does not have a 3G module. We work with the default
codec configuration (640x480, 30 fps, 3 Mbps). Both the software and
hardware decoders can decode at the configured frame rate. The
measurements also show that the 3G module consumes additional 30%-45%
To measure the impact of touch screens, we develop an Android
application that does nothing but accepting gestures from a user. The
accepted events are dropped immediately. We run this gesture
application on both mobile devices for 3 minutes and collect the CPU
utilization and power consumption. We continuously slide the touch
screens during the second minute, and leave the mobile devices idle in
the first and last minutes. To isolate the resource consumption due to
touch screens and event processing, we also write a replayer
tool that injects the touch screen events to the gesture application. We
run the gesture application and log all the timestamped events. We then
inject the events to the gesture application using replayer,
and compare the two measurement results. Figure 8 gives
the CPU utilization and power consumption over time. This figure shows
that the touch screen (including event processing) and event processing
(only) consume similar amount of resources. This indicates that the
event processing is energy-hungry, while the touch screen consumes
negligible amount of additional resources.
That is, touch screen and event processing is not free (in terms of
energy consumption), though it is usually overlooked. Our measurements
show that event processing consumes additional 6%-10% power. More
in-depth analysis along this line is among our future tasks.
Figure 7: CPU utilization and power consumption for different
wireless access links.
Figure 8: CPU utilization and power consumption for
handling control events.
In this paper, we implement a testbed based on a real mobile cloud
gaming platform GamingAnywhere [9,10]. We conduct
extensive experiments to measure the client performance and energy
consumption. Our measurement results lead to the following main
Running mobile cloud games is more energy efficient than native
mobile games. Our experiments indicate that mobile cloud games reduce
CPU utilization by half, and save energy by 30%.
The hardware video decoders are advantageous in that higher
workloads (ie., higher frame resolution or higher encoding bitrate)
would not increase CPU workload and energy consumption. However, the
hardware video decoders in our study always buffer 1-2 frames, which
are less ideal to fast-pace games.
The video codec parameters (bitrate, frame rate, and resolution)
impose different levels of impacts on CPU utilization and energy
consumption. The frame rate affects the most, while the resolution
affects the least.
Two other energy-hungry components are identified: (i) the 3G
cellular module consumes 30%-45% more energy compared to WiFi module
and (ii) the event processing of touch screens consumes nontrivial CPU
and energy resources as well.
We believe that such findings point out helpful design recommendations
for future researchers and developers of the emerging mobile cloud
This work was supported in part by the Ministry of Science and
Technology of Taiwan under the grants
MOST 103-2221-E-019-033-MY2 and
We would also like to thank the reviewers for their insightful
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This paper is an extended version of a workshop short paper
that appeared as .
Sheng-Wei Chen (also known as Kuan-Ta Chen) http://www.iis.sinica.edu.tw/~swc
Last Update September 28, 2019