Abstract: –

vision of 5G Mobile networks lies in the provisioning of high throughput (over
10GB/s data rate), low latency (less than 1m for radio link latency) and
connecting more devices (over 1M terminal per square kilometer). This is driven by the need
for the Internet of Things (IoT) applications (e.g. smart cites smart homes),
rich immersive multimedia applications (e.g. live video streaming) and the
tactile Internet. To achieve 5G targets, Mobile edge computing (MEC) is
regarded as a vital solution to provide
satisfactory Quality of Experience (QoE) for multimedia services. Although
video streaming applications are seeking more bandwidth and exorbitant video quality.
Particularly for virtual reality applications or video on demand (VoD), and
adaptive streaming protocols like MPEG-DASH (dynamic adaptive streaming over
HTTP) lets video quality to be flexibly adapted, means that the video quality
will degrade when network condition is affected. But that is not an option for
the video quality to go down when the application itself needs to assure high
video quality all the time. In this paper we present Mobile Edge Computing
(MEC) scheme for enabling video caching to the closest node of the end user,
rather than going back to the cloud every time the video is requested in this
way we will reduce the traffic to the cloud and the content will be closer to
the end users, to optimize QoE to the end user. 
So in this way, we implemented the
concept of bringing the content closer to the user, and free the backend-core
from traffic.

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Introduction: –

It is becoming a Fact that in
next few years video streaming application will rule the internet traffic, and
Cisco Visual Networking Index predicting that video applications will dominate
75% of overall internet traffic by 20201, over the years video content
provides e.g. YouTube, have been using MPEG-DASH (Dynamic Adaptive Streaming over HTTP) standards to deliver
streaming services. 2 Meanwhile MPEG-DASH has many advantages like
flexibility through on-the-fly quality adaptation and it is simple to add it
over the existing HTTP infrastructure, and the fact that MPEG-DASH uses
transmission control protocol (TCP) is acting like a double-edged sword, the
first edge is that it can avoid video degradation that is caused by losing
I-Frame or other things. And the second edge is that when a wireless user
equipment (EU) streams videos, there will be 2 network segments on the
end-to-end path that will have different characteristics, 1) radio access
network (RAN) which is wireless. 2) Mobile core network and the internet that
in most cases are wired, the wired segments have higher bandwidth-delay-product
(BDP) due to the high-capacity over-provisioned backbone link and long latency
due to long distance that the data transport between the global Internet.

One of the major networking
problems is the insufficiency of structured resource management schemes to
provide Quality of Service (QoS) and Quality of Experience (QoE) support for
real-time applications, primarily in networks that can be affected by delay or packet
loss. E.g. the video or VOIP services are not tolerant to packet loss that
should not more than 1%, especially if
the system is using compressed codecs e.g. G.722, and the latency is also
playing a major impact on the services which should be less than 150ms between
two end-points 3.


The delivery of mobile content, especially in high definition (HD) videos
with high resolution, has become one of the main
topics to add to the context of 5G network development  in the future  It has become a fact  that 5G is not only about increasing network
capacity, but also about quality of service (QoE) through adding  necessary network intelligence in all type of
network environments. With the recent
development of new networking paradigms, in particular,
Mobile Edge Computing (MEC 4), it is envisaged that content awareness and
intelligence can be embedded at the mobile network edge (e.g. at eNodeBs, or
eNBs in LTE networks) in order to achieve desirable user quality of experiences
(QoE) against various dynamicity and uncertainty in the network ecosystem. in
the sense that the network is able to understand specific content delivery
requirements and conditions when performing content handling operations.


The main objective of this paper
is to optimize QoE to the end user using MEC regardless of the mobility that
will be done by the end user the approach ensures that the user is always
served from the closest node to him but when the network condition is
decreasing it will start serving from the next closest node to the user if it
is in range which is done by cashing the content to the node that is serving
the end user 

























Related work: –

setup: –

to show edge-based video
streaming and movement we used Ubuntu
16.04 LTS desktop to and two laptops with the same host type as the desktop to
do this testbed. The Desktop itself is used to simulate cloud environment using
Devstack (OpenStack) which includes all
(network, storage, compute, and controller) in the same node, and then we
launched an Instance with Ubuntu cloud operating system inside it which is a
minimal Ubuntu image to host the MPEG-DASH video server, for the video content we used Big Buck Bunny open movie 5, and
Apache as a web server. The IP was taken
from a private IP range pool, and then we specified the route to know where to
go outside the main Desktop, the Desktop is connected to a router with IP of Since the Devstack instance is
having a private IP address, it is necessary to make interaction with the outside
network possible by defining the static routes in the first router. As defining
the set of IP Table rules to forward the traffic from the public to private

and to emulate the Edge servers
one virtual machine is installed in each of the laptops which they use Ubuntu
16.04 and the connection between the two laptops is done by using an ethernet
switch, and both of the virtual machines use the
same virtual environment to ensure that
they both act like edge access points. Containers have been created in both virtual machines, using LXD containers
and squid has been instilled inside both them to act as a web server, and it has been configured to act as a proxy to the back-end cloud with streaming
and caching functionality which is hosting the MPEG-DASH video server. And as
the first router, it is mandatory to define the
set of IP Table rules to forward the traffic between the first router and the
second router. After configuring the routers now they can realize the right
path to take to reach their destinations. it is worth recalling that the
aim of this testbed setup is to optimise the QoE to the end user using MEC and
regardless of the mobility.

Figure-1- Testbed setup

To perform the test, a container
is configured with all the futures mentioned above, and connected to a ethernet
router, then a mobile phone has been used and connected to the same network as
the LXD container by  Wi-Fi router, then
the user starts to browse the video using the URL of the container , and as we
mentioned above that the container acts like a proxy to the backend-cloud so
the container forwards the request to the backend-cloud to deliver the content
of the video to the container, in this stage the container will start caching
the video segment that has been sent from the backend-cloud for further uses, so
next time that the user asks for the same video the container will use it is
own cashed segments regardless  of the
cloud if it is in range or not, by doing
that we implement the conept of bringing the content closer to the client and
making the backend-cloud free of traffic or less traffic,  the next step is the migration between two
nodes which will happen according to two options, the first condition is, if
the network condition is bad, and the second one is movement when the user is
getting closer to the next node, starting from the scenario shown in figure 1,
we started to browse the video from the first node in a perfect condtion network, the node started fetching the
segements from the backend-cloud and it was cashing every segment that it
gets.then we used a script to decrease the bandwidth and overload the network
and the video quality went down.

Now, we repeat the network behavior, but this time with the migration option,
that means after the user starts to browse the video from the first node, after
a fixed amount of time we will   overload the network and decrease the
bandwidth, alongside that a script is used to check the buffer size and whenever
it reaches a critical percent that it will affect the video quality, the migration
will start, and the user will be getting the video segments from the new node,
without letting the video quality go down. Rsync was used to compare the cached
files between the containers, so both of the containers will have the same
cached segments, in this way if another user asked for the same video the nodes
will use it is own segments rather than going back to the backend-cloud, in
this way we even reduced the traffic to the cloud so the load will be less in
it, and the user is totally unaware of the fact that the streaming node has
been changed they are just enjoying the streaming , results are discussed in
the next section.
















In our tests, we considered 3 major
impacts on video quality which are Buffer size,
Bitrate, And Throughput. Tests have been done without migration and with
migration as mentioned in the use cases above, as shown in figure -2- it is obvious that when there is no migration is
going on and the network quality is  reducing
the video buffer size is reducing too , and
in the other hand when migration option is there,
as soon as the node knows that the network condition is reduced the migration
will happen and the buffer size will drop for a small time then go back to the
normal levels again without letting the user notice any changes on his/her side.

Figure -2- buffer size

Regarding video bitrate that the
user is watching, we did more than one test to choose the right time of
migration without letting the user face a low-level
video or causing the video to pause or
stop completely, the script that we generated is checking the buffer size from
the first second that the video has been streamed so the normal buffer size for
a perfect condition video in 2400 bitrate
is between 29-32. And the script is always checking the buffer size and whenever it falls below 25 it will start the
migration to the new node. In this case,
the buffer size will go up to the normal range after a short period of time. As
shown in figure 2, figure 3 shows the
bitrate for both conditions migration and
without migration.

Figure -3- Bitrate

Migration has been tested to be
done at different times e.g. when the buffer level hits 15 or less or 18,20and
25. and results show that to maintain a
good quality for the client the migration needs to start when the buffer size
hits 25 or fall below 25 so the user will not witness
any change in the quilty  of the video. Down
below is graph 4 which shows throughput
levels in both migrations and without
migration network.

Graph -4- throughput












  “Cisco VNI mobile forecast,
2015–2020,” Cisco, Syst., Inc., San Jose,

CA., USA, 2016. Online. Available:


Accessed on: Jan. 2017.

Information Technology—Dynamic
Adaptive Streaming Over HTTP

(DASH)—Part 1: Media Presentation
Description and Segment Formats,

ISO/IEC 23009-1:2014, 2014. Online.
Available: http://www.iso.org/


3-      T. Szigeti and C. Hattingh, End-to-End QoS Network Design:
Quality of Service in LANs, WANs, and VPNs. Cisco Press, 2004.

4-      ETSI. Mobile Edge Computing: A key technology towards 5G
(Whitepaper). Available at http://www.etsi.org/images/files/ETSIWhitePapers/ ETSI wp11 MEC
a key technology towards 5g.pdf

5-      ‘Big Buck Bunny movie’ http://www.bigbuckbunny.org

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