We conduct real experiments to quantify user satisfaction in mobile cloud games
using a real cloud gaming system built on the open-sourced GamingAnywhere. We share our experiences
in porting GamingAnywhere client to Android OS and perform extensive experiments on both the
mobile and desktop clients. The experiment results reveal several new insights: (1)
gamers are more satisfied with the graphics quality on mobile devices, while they are
more satisfied with the control quality on desktops, (2) the bitrate, frame rate,
and network delay significantly affect the graphics and smoothness quality, and (3)
the control quality only depends on the client type (mobile versus desktop). To the
best of our knowledge, such user studies have never been done in the literature.
Categories and Subject Descriptors: H.5[Information Systems Applications]: Multimedia Information Systems
General Terms: Measurement
Keywords: Cloud games, mobile games, performance evaluation, user studies
Increasingly more cellular users own smartphones and tablets, e.g., majority
( > 50%) of cellular users in U.S., U.K., China, Australia, South Korea, and Italy
have switched from feature phones to smartphones . Many of
these mobile users play mobile games, e.g., 59% of the mobile users have played games
on their mobile devices in 2011 . Moreover, the mobile gaming market
share is expected to grow to 16 billion USD by 2016 . Compared to console
and desktop games, mobile games are often less visually appealing due to the limited
CPU/GPU power, memory space/speed, network bandwidth, and battery capacity, which
may drive serious gamers away from mobile games.
Cloud gaming renders, captures, and encodes game scenes on powerful cloud servers,
and streams the encoded game scenes in real time over the Internet to less capable
client devices. The client devices collect gamers' inputs and send them back to the
cloud servers, in order to interact with cloud games . Although cloud
gaming appears to be very suitable for resource-constrained mobile devices,
commercial cloud gaming solutions, from OnLive, GaiKai, G-Cluster, OTOY, Spoon,
T5-Labs, and Ubitus, are mostly accessible on: (i) PCs using native or web
applications or (ii) TVs using set-top boxes. We believe that mobile cloud
gaming has not been widely deployed at least due to the following two serious
Steep development cost. Traditionally, the game industry is
conservative and careful about adapting to new consoles and development
environments, for the sake of cost control. However, most of the cloud gaming
platforms [14,1,4] dictate proprietary SDKs, which in turn
discourages the game industry from adopting cloud gaming. Thus far,
GamingAnywhere  is the only cloud gaming platform in the
literature, which is fully transparent to games. That is, GamingAnywhere frees the game
industry from porting their games to new SDKs.
High bars on user satisfaction. Gamers demand for high-quality game
scenes and low response delay, which are inherently difficult,
especially on resource-constrained mobile devices. Existing measurement
studies  concentrate on low-level objective metrics, and how these
measurement results affect user satisfaction is not well understood.
In this paper, we addressed the two challenges in two steps. First, we optimize
GamingAnywhere  client for mobile devices. GamingAnywhere is the first
open-source cloud gaming platform, designed for high extensibility, portability, and
reconfigurability. Optimizing GamingAnywhere client on mobile devices is non-trivial due to
tight resource constraints, and specialized CPU/GPU architectures and mobile OS's.
In this paper, we share our experiences in porting GamingAnywhere client to Android, while
porting to other mobile OS's is also possible. To the best of our knowledge, our
mobile client is the first mobile cloud gaming client that is transparent to games,
i.e., gamers can use our client to play any PC games on their mobile devices.
Details on our porting experiences are given in Section 3.
Second, we conduct user studies using the real mobile cloud gaming system to analyze
how different configurations affect the gaming experience. We consider
configurations with several parameters: resolution, frame rate, bitrate, and network
delay. Both resolution and frame rate affect the visual quality under a given
bitrate; however, the precise impacts depend on many other factors including game
genres and device types. For example, car racing games may need higher frame rates,
while strategy games need higher resolutions. Moreover, response delay greatly
affects user experience , and network delay is a component of
response delay . The network delay does not seem to be controllable at
first glance. This observation however is not always correct, for example: (1) cloud
gaming platforms may place games in different data centers to control network
latency  and (2) mobile clients may choose different access networks
(such as 4G/LTE over 3G networks) for lower network delay. Our extensive user
studies lead to several key observations:
Overall. Gamers demonstrate diverse user satisfaction levels on desktops
and mobile devices. Generally, gamers are more satisfied with: (1) the graphics
quality on mobile devices and (2) the control quality on desktops.
Impacts of configurations on user satisfaction. Encoding bitrate, frame
rate, and network delay are the three most critical system parameters affecting the
graphics and smoothness quality.
The rest of this paper is organized as follows. Section 2 surveys
the current mobile cloud gaming research. Section 3 presents
our experiences in porting GamingAnywhere client to Android OS. This is followed by the
detailed user studies in Section 4. Section 5
concludes the paper.
2 Related Work
All commercial cloud gaming platforms are closed and proprietary, and thus cannot be
used in our user studies. GamingAnywhere  is an open-source cloud
gaming platform consisting of three entities: the server, client, and portal. To use
GamingAnywhere, a gamer first logs into the portal and selects a desired game. The portal then
launches the chosen game and server on the same (virtual) machine in the cloud. The
portal also notifies the client to set up connections to the server, which starts a
game session. In the current paper, we port the GamingAnywhere client to mobile devices, and
use it to conduct user studies, which is not possible on commercial cloud gaming
Several research projects [14,1,4] attempt to enhance the
low-level performance of mobile cloud gaming. Shu et al.  propose to
employ 3D warping technique to perform light-weight image processing at mobile
clients, so as to increase coding efficiency and mitigate network delay. Hemmati et
al.  propose to selectively encode game objects to reduce the required
network bandwidth and processing power without affecting gaming quality. Cai et
al.  propose to dynamically allocate cloud resources to meet the needs
of mobile gamers, who often move across diverse contexts. These
approaches [14,1,4] are not transparent to games, requiring game
developers to adopt proprietary SDKs. In contrast, GamingAnywhere supports all PC games as-is.
Most existing measurement studies on cloud gaming are done using desktop
computers [2,13,7,8]. Chen et al.  and Shea
et al.  concentrate on objective quality metrics, while Jarschel et
al. [7,8] consider subjective quality metrics. Different from these
studies [2,13,7,8], we take mobile cloud gaming into
consideration. The performance of mobile cloud gaming has only been measured
recently . Lampe et al.  consider three low-level
performance metrics: latency, energy, and cost, trying to demonstrate the
feasibility of mobile cloud gaming. In contrast, we study how different
configurations affect user satisfaction via extensive user studies, which is a key
research problem to optimize mobile cloud gaming.
We emphasize that our GamingAnywhere client is a transparent and open
mobile cloud gaming platform, which allows us (and other researchers) to use
off-the-shelf PC games for experiments.
3 Porting Client to Android
Challenges. Mobile devices are
connected to the Internet via WiFi or cellular networks, which incur long
network latency and dictate an extremely efficient GamingAnywhere mobile client.
Furthermore, computational power and battery life are two critical constraints on mobile devices.
Therefore, modern mobile devices often come with built-in GPUs and
audio/video hardware codecs, which are different from general-purpose CPUs.
Last, due to small screen sizes, designing a unified game controller for all game
genres is quite difficult. Details on how we address these challenges are given
Figure 1: The architecture of the mobile client.
Architecture. We implement the mobile GamingAnywhere client on Android OS.
Figure 1 reveals the software architecture of the Android client.
The skeleton of our mobile client is
written in Java, but some
components are implemented as loadable shared
objects in native C and C++ codes. The native codes are mainly used for two
purposes: (1) to bridge the codes between Java and GamingAnywhere library,
and (2) to minimize maintenance overhead by sharing existing desktop client
(a) Edit profile for Limbo.(b) Choose profile and controller pad for Limbo. (c) Play Limbo with Limbo's controller pad. (d) Play Mario Kart with N64 controller pad.
Figure 2: Screenshots of the mobile client.
Networking component. This component is responsible for several operations, such as
setting up RTSP connections, receiving RTP packets, and
transmitting control packets. The same software component also manages
audio/video buffers, parses the audio/video packet headers,
extracts the MIME-type, and configures the decoders.
The networking component is implemented in the native code, and integrated with the Java skeleton via Java Native Interface (JNI).
Decoding. The mobile GamingAnywhere client supports two types of codecs: (1) software codecs and (2) Android built-in codecs.
The audio and video codecs are configured independently. Hence, there
are four paths in Figure 1, i.e., a-path#1 and
a-path#2 for audio and v-path#1 and v-path#2 for
video. Software codecs are the same as those of GamingAnywhere desktop client.
In contrast, the built-in codecs are provided by
the Android MediaCodec framework, which is
available on Android 4.1 or later.
MediaCodec framework is only
accessible from Java side. Therefore, on receipt of an audio or video
packet from the network, the packet is parsed in native codes and
then passed to Java for decoding.
For audio packets, decoded raw audio frames can be retrieved from the framework and
then used for playback. For video packets, decoded video frames are
directly presented to the user through a pre-created surface object. That is, the decoded raw video frames
are not accessible to applications, and
are automatically resized to fit the resolution
of the surface object.
Rendering. The raw audio frames are always rendered with the
AudioTrack framework in Java, no matter whether built-in or software
codecs are used. When software codecs are used, the decoded video frames have to be first converted from YUV420P to RGB565 format.
We then employ OpenGL ES
library to resize and render decoded video frames. More specifically, each
decoded frame is treated as a texture and drawn on an OpenGL surface.
The MediaCodec framework and OpenGL ES adopt different surface objects:
SurfaceView and GLSurfaceView, respectively.
Controllers. The controller pads are transparent overlays on top of the video
surface. Designing a unified controller to support all game genres is out of the
scope of this paper. Instead, we implement three representative controllers for
gamers to choose from. The implemented controllers are designed for: (1) Nintendo 64
emulator, (2) Nintendo DS emulator, and (3) Limbo.
Practical concerns. Our mobile GamingAnywhere client is affected by two practical
limitations. First, the H.264 intra-refresh option is not supported by some
built-in codecs, especially those hardware-accelerated codecs. This option increases the robustness of video streaming under lossy wireless channels. We have tested this
on several Android devices and found that at least the first generation Nexus 7
tablet does not support the option: the built-in codec freezes shortly after the
video playout starts. A work-around is to disable intra-refresh on the GamingAnywhere
server at the expense of degraded robustness.
Second, some built-in codecs are not included in system images built from Android
Open Source Project (AOSP). This is because these codecs require drivers that are
not open-source. In that case, the mobile GamingAnywhere client only shows a blank screen.
Prototype implementation. Upon installing the client, a user first creates a
profile using the user interface shown in Figure 2(a). Each profile
consists of configurations, like server address, server ports, and codec parameters.
Then, the user selects the desired profile and controller using the interface shown
in Figure 2(b). Once the Connect button is pressed, the client
connects to the server. Figures 2(c) and 2(d) show the
rendered game screens with user-specified controllers for Limbo and Mario Kart,
In this section, we present our experiments that were designed to evaluate the user
satisfaction in mobile cloud games. We focus on the differences in user
satisfaction introduced by the mobile devices (i.e., compared with desktop cloud
gaming), and the effect of various system parameters on the perception of mobile
4.1 Experiment Setup
Figure 3: Our experiment setup.
The experiment environment consists of three hosts: a server, a desktop client, and
a mobile client. We set up our GamingAnywhere server on a Windows 7 desktop with an Intel Core
i7-870 Processor (8 MB cache and 2.93 GHz) and 8 GB main memory. The desktop client
runs on a Windows 7 desktop with an Intel Core2 Quad Processor Q9400 (6 MB Cache and
2.66 GHz) and 4 GB memory, and the mobile client runs on a Samsung Galaxy Nexus (1.2
GHz dual-core CPU, 1 GB memory, AMOLED 4.65-inch screen, and 720p resolution) with
Android 4.2.1. The desktop client and the mobile client were connected to the server
via a Gigabit Ethernet LAN and an 802.11 wireless LAN, respectively. We ensured
that both LANs are under-utilized during our experiments for fair comparisons. The
experiment environment is shown in Figure 3.
4.2 Experiment Design and Data Collection
Table 1: Selected Games
Mario Kart 64 (kart)
Super Mario 64 (mario)
Super Smash Bros. (smash)
We select four games, as listed in Table 1, in different genres for
this study. Three of the four games are from the Nintendo 64 platform, and we use
the mupen64plus emulator to run those games. To study the user satisfaction
under different configurations, we vary four system parameters: video resolution
(resolution), encoding bitrate (bitrate), frame rate, and network delay (delay). In
the experiments, we vary each parameter with three levels while keeping the other
parameters to their default values. For resolution settings, we change the game
resolution options in the mupen64plus emulator for the three Nintendo 64
games; unfortunately, Limbo does not allow resolution setting, so we can only run
Limbo with 1280x720. We configure the frame rate and encoding bitrate at the GamingAnywhere
server. As to the network delay, we employ
ipfw1 to increase the network delay of the traffic between the server and the
clients. Detailed settings for each factor are listed in Table 2
with the default values highlighted in boldface.
Table 2: A Summary of Experiment Settings
15 (5 females, 10 males)
21-34 years old (mean 26.2 years old)
Total game sessions
Total gameplay time
640x480, 960x720, 1280x960
1 Mbps, 3 Mbps, 5 Mbps
5 fps, 20 fps, 50 fps
0 ms, 150 ms, 300 ms
Default values are highlighted in boldface.
Screen resolution for Limbo is fixed at 1280x720.
We conduct two user studies: (1) desktop cloud gaming and (2) mobile cloud
gaming. Each subject participates in both studies in random order, and each
study consists of the four games in random order. Every subject plays each game
under various configurations: 9 configurations for the Nintendo 64 games and 7
for Limbo, as Limbo disallows resolution changes.
Therefore, each player was asked to
play a total of (9 ×3+7) ×2=68 game sessions in our experiment. We require each
game session to last for a minute, and the game is automatically terminated. Each subject is then prompted to evaluate their gaming experience in terms of the following three aspects
on a five-level MOS scale:
Graphics: The visual quality of the game screens.
Smoothness: The negative impact due to delay, lag, or unstable frame rate observed during game play.
Control: The quality of the control mechanism (i.e., keyboard for desktops and touch screen for mobile devices).
We recruited a total of 15 subjects to participate in our experiments and summarize
the collected dataset in Table 2.
4.3 Mobile versus PC: Which One Is More Satisfying?
Figure 4: Overall MOS scores under different system parameters and client devices.
To compare the overall user satisfaction of GamingAnywhere clients on different devices,
we first compute the overall MOS scores across all games and configurations on
each device. Figure 4 gives the average results with 95% confidence levels. We make the following observations.
Table 3: Student's One-tailed t-tests for the Differences Between Desktop and Mobile Cloud Gaming
Figure 5: MOS scores associated with each factor setting and client device.
Graphics. Interestingly, the mobile client leads to significantly higher scores, although
the mobile display is much
smaller. This observation may be attributed to two
reasons. Firstly, the subjects may have
lower expectation on graphics quality in mobile games. Generally, mobile
devices have relatively low computing and graphics rendering power compared to
high-end desktops, as a result, most mobile games do not even try to compete with PC
games in terms of graphics quality. While GamingAnywhere provides basically identical graphics
qualities on both mobile and desktop client, they are evaluated with different
standards. Secondly, as the physical dimensions of desktop and mobile devices are
quite different (i.e., 27 versus 4.65 inches), the graphics imperfectness due to
video encoding/decoding and network loss, such as blur, blocking effects, and mosquito noise,
tends to be more easily spotted by subjects on
desktop screens. It appears that the subjects rate the graphics quality based on a
"minus principle." In other words, the satisfaction levels are rated based on the
flaws observed rather than on the absolute quality of a stimulus.
Smoothness. Overall, the smoothness of game play on desktop and mobile
clients is fairly close. This demonstrates that our GamingAnywhere client is well-tuned
on both desktops and mobile devices. Therefore, the software implementations do
not bias the experiment results on the graphics and control quality.
Control. The desktop client performs better than the mobile version in terms of
control. It is less surprising as
the selected games are not
specifically designed for mobile devices. On desktop computers, keyboards are capable
of complicated key-stroke combinations and can be easily used to simulate controls of other
platforms such as N64. In contrast, touch screens are much more restricted.
Next, we perform hypothesis tests to study how the device
differences affect user satisfaction while the subjects play games in different
genres. Table 3 lists the one-tailed t-tests for the scores from different client devices and games.
The table reveals that the differences in graphics and
control between the two clients are highly significant,
with p-values less than 0.001. It is also shown that the subjects are much more
sensitive to the graphics difference in Limbo than in Mario Kart.
This can be attributed to the nature of the two games: Mario Kart is a
fast-paced racing game, hence gamers may not pay too much attention on the
degradation in graphics quality; in contrast, Limbo is relatively static, and
gamers are sensitive to the graphics quality.
The other observation is that the subjects are
less sensitive to the difference in control in platform games (Limbo and
Super Mario) than in fighting (Super Smash Bros.) and racing (Mario Kart) games.
We believe this is because
fighting and racing games are faster-paced and have AI opponents
directly competing with you, therefore precise control is crucial for gamers to prevail in these games;
while in platform games, you have time to prepare for your every
action and some failed attempts are tolerable.
4.4 Impact of Various System Parameters
Table 4: ANOVA Tests for the Impact of Individual System Parameters on User Satisfaction
We report the average MOS scores (with 95% confidence levels) under different system parameters and
client devices in Figure 5.
Since there are three levels
for each system parameter in our experiments (except the 1280x720 resolution for Limbo), we
use one-way analysis of variance (ANOVA) model to test whether each factor
introduces significant effect on the user satisfaction in gaming.
We had to exclude some data from Limbo, as Limbo disallows resolution changes.
Table 4 gives the ANOVA results.
This table shows that bitrate is the most important parameter that affects
the graphics quality on both client devices. A deeper look reveals that the
bandwidth needed to stream the game screen under the default resolution and frame
rate is about 3.5 Mbps. When the bandwidth is not enough, data will be dropped and
the graphic quality will severely decrease. The drop in frame rate also degrades
subjects' perceived quality of graphics, although the impact is much weaker. One may
expect the resolution to be a significant factor to graphic quality, however, the
results show that it has only little if any effect on graphic quality. It is
probably because the selected games have substandard graphics details compared to
today's standards, therefore when the screens are upscaled to the same resolution on
the client, the differences are hardly noticeable. We will look into more details on this observation in
The smoothness of the games is affected by many system parameters. Not
surprisingly, network delay and frame rate impose significant impact on
smoothness, because lag and unstable frame rate directly result in low smoothness.
In fact, high network delay and low frame rate both lead to the same negative
impacts on gamers' reactions, which increases the gamers' levels of frustration. In
addition to delay and frame rate, our analysis indicates that low bitrate may also
affect the games' smoothness on mobile devices. We suspect that subjects may give
low MOS scores on smoothness when the graphics quality is extremely low, as they can
not play the game anyway. More detailed user studies to verify our hypothesis are
among our future tasks.
Last, the control quality is not affected by any of the system parameters.
Rather, it is affected by the client device types (desktops versus mobile
In this paper, we have presented a mobile cloud gaming system built upon GamingAnywhere. We
shared our experiences in porting a cloud gaming client to Android, which are also
applicable to other mobile OS's. We used the mobile and desktop clients to conduct
extensive user studies, so as to understand the implications of different system
parameters (resolution, frame rate, bitrate, and network delay) on user experience
(graphics, smoothness, and control). The experiment results reveal that: (1)
gamers are more satisfied with the graphics quality on mobile devices and the
control quality on desktops, (2) the bitrate, frame rate, and delay affect the
graphics and smoothness quality the most, and (3) the control quality is mainly
affected by the device type. Several future research directions are possible. For
example, we have not observed the impact of resolution on user experience, which may
be shown in larger-scaled user studies.
W. Cai, C. Zhou, V. Leung, and M. Chen.
A cognitive platform for mobile cloud gaming.
In Proc. of the IEEE International Conference on Cloud Computing
Technology and Science (CloudCom'13), pages 72-79, Bristol, UK, December
K. Chen, Y. Chang, H. Hsu, D. Chen, C. Huang, and C. Hsu.
On The Quality of Service of Cloud Gaming Systems.
IEEE Transactions on Multimedia, 16(2), Feb 2014.
M. Claypool and K. Claypool.
Latency can kill: Precision and deadline in online games.
In Proc. of ACM SIGMM Conference on Multimedia Systems
(MMSys'10), pages 215-222, Phoenix, Arizona, February 2010.
M. Hemmati, A. Javadtalab, A. Shirehjini, S. Shirmohammadi, and T. Arici.
Game as video: Bit rate reduction through adaptive object encoding.
In Proc. of ACM International Workshop on Network and Operating
Systems Support for Digital Audio and Video (NOSSDAV'13), pages 7-12, Oslo,
Norway, February 2013.
H. Hong, D. Chen, C. Huang, K. Chen, and C. Hsu.
QoE-aware virtual machine placement for cloud games.
In Proc. of IEEE Workshop on Network and Systems Support for
Games (NetGames'13), Denver, CO, December 2013.
C.-Y. Huang, K.-T. Chen, D.-Y. Chen, H.-J. Hsu, and C.-H. Hsu.
GamingAnywhere: The First Open Source Cloud Gaming System.
ACM Transactions on Multimedia Computing Communications and
Applications, pages 1-25, Jan 2014.
M. Jarschel, D. Schlosser, S. Scheuring, and T. Hobfeld.
An evaluation of QoE in cloud gaming based on subjective tests.
In Proc. of International Conference on Innovative Mobile and
Internet Services in Ubiquitous Computing (IMIS'11), pages 330-335, Seoul,
Korea, June 2011.
M. Jarschel, D. Schlosser, S. Scheuring, and T. Hobfeld.
Gaming in the clouds: QoE and the users' perspective.
Mathematical and Computer Modelling, 11-12(57):2883-2894, June
U. Lampe, R. Hans, and R. Steinmetz.
Will mobile cloud gaming work? Findings on latency, energy, and
In Proc. of IEEE International Conference on Mobile Services
(MS'13), pages 960-961, Santa Clara, CA, June 2013.
Mobile gaming, July 2011.
The mobile consumer: A global snapshot, February 2013.
PopCap games mobile gaming research, June 2012.
R. Shea, J. Liu, E. Ngai, and Y. Cui.
Cloud gaming: Architecture and performance.
IEEE Network Magazine, 27(4):16-21, July/August 2013.
S. Shi, C. Hsu, K. Nahrstedt, and R. Campbell.
Using graphics rendering contexts to enhance the real-time video
coding for mobile cloud gaming.
In Proc. of ACM Multimedia (MM'11), pages 103-112, Scottsdale,
AZ, November 2011.
1. ipfirewall (or ipfw in short) is a FreeBSD
IP packet filter and traffic accounting facility. There is a port of ipfw
and the dummynet traffic shaper available on Linux, OpenWrt and Microsoft
Sheng-Wei Chen (also known as Kuan-Ta Chen) http://www.iis.sinica.edu.tw/~swc
Last Update May 23, 2017