Efficient Cloud Gaming Scheme Using Scene Objects Adaptation

International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
DOI:10.5121/ijcsit.2017.9215 165
EFFICIENT CLOUD GAMING SCHEME USING SCENE
OBJECTS ADAPTATION
Ahmad A. Mazhar
Department of Computer Science, Saudi Electronic University, Taif Branch, KSA
ABSTRACT
The last decade witnessed wide-spread of internet and on-line gaming. Improving the broadband
limitations is considered one of the most important factors that led to this extent. The recently huge usage
of internet applications and games also led to grow up more the usage of handhelds and smart phones as
well. This improvement of internet speed with the limitations of the smart end devices led to rely more on
processing games away from these limited devices to robust processing centers in a technique so-called
Cloud Gaming. The growth of players number and game content request new ideas of bit rate reduction of
the streaming video of these on-line games. In this paper, a new technique has been proposed to reduce the
requirements needed by scene customization encoding with negligible impact on playing quality..
KEYWORDS
Cloud Gaming, DSIS, Content Adaptation, H.265, Video Encoding, Video Streaming
1. INTRODUCTION
The term Cloud Gaming is upcoming to be one of the most important terms in on-line gaming as
it implements cloud computing benefits for gaming. The idea behind cloud gaming is by running
the game as real-time application on away gaming centers while streaming the needed
requirements to the user’s limited device [1, 2]. The user’s device are only required to process a
specific amount of data that reflects user’s reactions to the game. The huge amount of data
processing such as 3D graphics rendering, are not necessary to be done on the client’s device [3].
The streaming of a game can be done typically by streaming objects and graphics to the player
along with updated messages. This approach consumes a lot of bandwidth and needs higher
requirements for users’ devices. Another way is done by encoding the game as a video stream
before it is communicated with users. The third approach depends on streaming game video and
the content graphics in hybrid technique. All previous approaches are considered efficient but
require high hardware specifications by the end-user [4].
The new trend of on-line gaming and the term of cloud gaming is to minimize dependency on the
end-user hardware. The end-user should not tolerate the huge amount of processing on his/her
device. A high-speed internet connection is the only thing that the end-user needs to have.
Fortunately, the vast broadband improvement makes this requirement available in different places
around the world [5, 6, 7].
According to this technique, end-user needs only to send control inputs of a game to the game
server and receives the game video on his own device. The game texture is encrypted and sent to
the user as an encrypted video sequence. In this case, the game server on the cloud performs the
time consuming processes such as video compression and objects rendering. Thus, high internet
speed and ability of high quality video display are the only requirements by the user side.
However, new platforms are needed to be designed for these upcoming techniques [8, 9, 10].

166
The new video gaming trend deals with the game as a video sequence not as a game and can be
called as a Game as Video technique. The game as video technique provides also more
advantages than only less hardware requirements by user. The use of console gaming is not
required where users need only to deal with a video sequence with only relatively cheap
controllers such as joystick. Users should only have a device to display video with ports for such
controller, if needed, and no need for updating consoles or spending money on buying game
discs. In this case, user’s device can be a smartphone, tablet or any other device with video
displaying ability.
Even though the computer gaming has grown up hugely, the number of mobile gamers has
increased throughout the forgoing years. That indicates on the increasing of the importance of
exploiting this field by game developers. In the current market, there are many cloud gaming
services available. Some of these services depend on PCs with a window application such as
OnLive. However, some PCs services depends on a web application as in GaiKai [11]. Modern
services such as GameNow and PSNow became more competitive in this field of gaming [12, 13,
14, 15]. TV accesses services that are more modern, which use special connectors and controllers
to make users able to play by just connecting these TVs to the Internet without needing a game
console. However, the most modern and widespread services depend on mobiles with cloud
gaming. This is one of the most important and updated services that will have more focus in the
future. Based on the wide spread of smart phones in addition to the increased number of mobile
gamers, cloud gaming using mobiles and handheld computers became more popular and easy to
use by users. Only in the USA, the number of mobile gamers raised from 78 million in 2009 to
181 million in 2016. These numbers are very important indicators on the fast growth of mobile
game players and led to consider this growth as the fastest in the field of digital gaming. In the
business market, the mobile gaming has been under the focus of game developers and industry
observers. According to the ABI research report, the mobile gaming revenues will grow to more
than 20 billion USD by the end of 2017. Since both industry and technology indicated that online
gaming is an active and potential upcoming field, the mobile gaming will grow more and is
getting much popular. As a result, the cloud gaming is expected to become more usable because it
decreases the usage of hardware required by user. One more important factor is the decreasing of
gaming cost as the user can deal with the game developer directly.
As cloud gaming provides many benefits by reducing hardware required to run a game and
increasing revenue for developers, unfortunately, some problems advanced while applying this
technique. Bandwidth limitations and transmission latency affected poorly on the quality of the
service. This problem occurs as the most parts of rendering are done on the cloud and not on the
user’s device as in the conventional way. This increases demanding on video game streaming and
then the more bandwidth consuming and more network resources needed. As a result, the network
limitations will play an important factor on the game quality and enjoyment.
In this paper, a novel algorithm is introduced to control the bit rate streaming. The devised
algorithm focuses on adapting the content of a game scene to reduce the amount of data required
to be sent by selecting partial contents of the game scene. Reducing the amount of non-effective
objects that need to be transmitted will save network bandwidth and processing time.
The paper is organized as follows: section II presented a survey of the previous works in the
literature. Section III discusses the proposed algorithm with description. Intensive experiments
with result analysis are shown in section IV. Finally the paper is concluded in section V.

167
2. PREVIOUS WORKS
As a game can be streamed as a video sequence in the game video technique, there is a need to
adapt this sequence and to be able to implement it in different environments. In [16], a new
methodology to improve player’s satisfaction is presented. The proposed mechanism adjusts the
game parameters in a real time manner to improve the entertainment for the player. A commercial
analysis of NVIDIA GeForce NOW game streaming is conducted in [17]. The analysis highlights
the problems of network limitations and proposed an adaption mechanism of game streaming
considering the end user network constraints. An empirical study to evaluate playing quality is
conducted and concludes that new future video encoding adaption strategies can be
recommended. In [18], a variant of Temporal Coherence Learning with geometric step size
changing was proposed. They showed its algorithm outperformed other ones with constant
change of step size. Some new researches focused on the cloud computing as an important factor
on speeding-up on-line video gaming. In [19], the paper minimized the end-to-end distortion of
real-time communication of the video and devised a new technique to minimize the loss of rate
approximation and source rate control. In [20], a depth analysis of many factors that affect game
players have been studied and analyzed. As a result, a high accurate player experience was
predicted for both long and short game segments. As the cloud computing is increasingly used in
different computing areas, cloud gaming is also becoming an insistent to solve huge number of
thin clients who use online gaming. In this circumstance many techniques were proposed and
devised to improve cloud gaming. A new request dispatching algorithm was proposed in [21].
The algorithm assigns the required requests after predicting ending times of a game session. The
predictions were conducted using neural network algorithm. In [22], authors presented a new
technique based on live rendering. The rendering supports inter-frame and intra-frame
compression. This technique reduced the demanding on the bandwidth and gained bandwidth
saving up to 73% compared to raw graphics streaming. In [23], a new algorithm to enhance the
cloud gaming platform was proposed. The algorithm considered problems in current cloud
gaming platforms such as supporting reconfiguration of video codecs dynamically. One important
result of the algorithm is maximizing average gamer experience. An extended work of the
previous technique called “CloudUnion” was presented in [24]. The work studied in-depth the
previous technique and its internal mechanism to solve hidden problems in other systems. An
intensive comparison study was conducted with a cloud gaming systems so-called
“GamingAnywhere”. The work results showed important factors on infrastructure and user
behavior that should be taken into consideration when designing a cloud gaming approach. In
[25], a hybrid cluster new technique depends on the user-level virtualization was proposed.
Firstly, according to resource consumption, they put games in two types: CPU-critical and
memory-critical. Secondly, several scheduling strategies were tested and the best scenarios were
recommended to be used.
Wireless networks became more popular during last few years. These wireless network
technologies have to become implementable for the modern multimedia applications. Video
streaming and cloud gaming are the most popular techniques that are being used over the
networks nowadays. To update wireless technologies to be able to implement such techniques,
many works were proposed. In [26], a new application was presented. The idea focused mainly
on error correction over TCP in wireless networks. They analyzed the frame-level distortion and
then proposed a new joint distortion estimation solution. The proposed technique showed
improvement on the quality of video streaming. In [27], an extended application-layer error
correction-coding framework was proposed and improved. Using simulation of real-time gaming,
the proposed solution was able to reduce frame rate drops and the random loss of packets.
Many ideas were proposed in the literature on the cloud gaming. However, few ideas were on the
idea of game texture adaptation. This work, in the next section, proposes a new idea on adapting
the video of a game to reduce resources and bandwidth consumption.

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3. PROPOSED ALGORITHM
The main idea behind this proposed idea is to render the effective 3D game objects on the cloud
instead of render it on the client side. Once the game objects are rendered and encoded on the
cloud with higher computer specifications, the clients are required only to receive a standard
encoded video stream and decode it in order to display on their screens. Therefore, we devised a
new idea to take down the streamed bit rate by embedding a fast encoding algorithm [28] and to
reduce encoding time by adapting the game scene. The idea based mainly on reducing the scene
contents by eliminating the non-effective objects. This content reduction will decrease both
rendering and encoding time. Also, it will minimize the required bit rate needed to be transmitted
between cloud and clients. The rendering and encoding techniques are run on the server as a
cloud gaming approach. The proposed encoding technique in [28] is recommended to be used for
faster encoding process. This is done by capturing a video scene and encode it on the cloud, then
transmit it to the client. The excluding of less important contents in a game scene depends mainly
on the player’s actions and events. Determining of these important actions are recorded and
captured by sensing player’s actions in each frame then prepare a dynamic list of important scene
contents. The auto-generated list of all objects is updated and sorted dynamically to give the
highest priority to the objects that are being used more by the player. The most important contents
that were noticed by our analysis stage were expected to be important in player decision, such as:
avatar, gun, monster in attack and other dynamic objects. Lesser-used objects are given lower
priority; the objects may contain background, sky, terrain and other complementary objects in the
scene. After determining the content and ordering objects according to its importance, a set of
parameters are specified to give criteria on what objects to be included in the rendering process.
The criteria based mainly on the specifications of the network capability and the end-user device
specification.
The proposed algorithm shows a combination of a fast encoding technique [28] with the proposed
object adaptation technique. As the adopted encoding algorithm is already more-rapidly, and it is
proposed to be run by the cloud, the encoding time became faster and acceptable for cloud
gaming. In addition, the adaption technique gives more flexibility to choose the important objects
depending on the resources available. As a result, these considerations will guarantee the optimal
player experience depending on the network and devices available. The proposed algorithm
proceeds as in the following outline.
Object adaptation algorithm:
1. Sensing player’s actions on the game.
2. Using sensed of actions, build/update a priority list of important objects in the content by
sensing objects that have been used higher by user.
3. According to player’s network and device capabilities, determine the most important
objects to share from cloud to player.
4. Using technique in [28], encode objects that were selected in Step 3.
5. Append encoded objects to the scene and stream to the player.
6. If game is still on, go to Step 1.
The proposed algorithm shows that the priority list is dynamic and updated automatically each a
set of frames. This leads to keep sensing the player’s actions and guarantees to determine the
importance of the objects related to the scene. Moreover, high content scenes will focus on the
most important objects that affect the player experience with minimum resources consumption.

169
4. EXPERIMENTS AND ANALYSIS
The proposed algorithm has been evaluated using Call of Duty game as it is considered one of the
most popular game in the world [29]. The proposed algorithm has been implemented to create a
priority list of objects in the game scenes. The highest priority objects have been encoded,
rendered and appended to the game stream. Some of the scene components were not selected and
have been eliminated as they do not affect player decisions. For example, some details of the
terrain and sky are not necessary while a player is standing and shooting on target. Figure 1,
shows three original and adapted scenes captured from the game during actual playing.
Figure 1. Original and adapted scenes
As seen in figure 1, the irrelevant components that do not affect player decision have been
eliminated. The game was played for 60 seconds, the playing scene was being captured using
Fraps [30] with 30 fps and 1024×920 pixels resolutions. The captured sequences were encoded
using [28] with the parameters shown in Table 1. The encoding algorithm and parameters were
chosen as they give high compression performance with acceptable encoding time.
Table 1 Encoding settings
Name of the Parameter Value
No. of references 5
No. of encoded frames 150
Motion vector search range 16
Motion vector resolution 1/4
Quantization parameter 28, 36
RDO On
GOP IPPP
Bit rate 1 Mbps
After the proposed algorithm was run, it showed interesting improvements on both encoding time
and streaming bit rate. The gained results were computed and compared to the original video
stream. The compares are shown in Table 2.

170
Table 2 Comparison between original and adapted videos
Original
Video
Adapted Video
Percentage of
Decrement
Average streaming bit rate 1172.5 Kbps 844.7 Kbps 27.9%
Average Encoding time per
Frame
56.3 ms 27.8 ms 50.6%
As seen in Table 2, the streaming bit rate of the adapted video is decreased by a rate of 27.9%
compared to the original one. In addition, the average encoding time is decreased from 56.3 ms in
the original video to 27.8 ms in the adapted video by a rate of 50.6%. Even though the saving in
both streaming bit rate and encoding time are significant, these savings are expected to be more
effective on the cloud as hundreds or thousands of players are served.
The encoding time is a significant factor while encoding real time games. However, combining
fast encoding time with an efficient adaption technique will lead to lower bit rate and then
reduced bandwidth requirements. The adapted video with fast encoding technique gives an
important contribution in the field of cloud gaming. As any small amount of bit rate reduction or
encoding time saving lead to massive saving on the cloud because the huge number of clients.
5. CONCLUSION
A fast object adaptation technique for cloud gaming was presented. The idea of selecting the
important objects in the gaming scene was exploited to eliminate less important and effective
objects. Experimental results showed that the proposed technique succeeds to reduce both bit rate
and encoding time by rates of 27.9% and 50.6% respectively. The proposed adaptation technique
is combined to a previous proposed fast encoding technique to achieve higher bit rate reduction
and network bandwidth saving.
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AUTHOR
Ahmad A. Mazhar was born in Amman, Jordan in 1980. He has been a member of the
College of Computing and Informatics at Saudi Electronic University since 2015. He has
more than eight years of academic experience. He received his Ph.D. in 2013 from De
Montfort University, UK; his Master’s degree in computer science from Al- Balqa' Applied
University, Salt, Jordan; and his Bachelor’s degree in computer science from Al-Zaytoonah
University, Amman, Jordan.

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