1. Effect of Varying Segment Size on DASH Streaming
Quality for Mobile User
Yomna M. Hassan
Mohamed M. Rehan
Intel Labs Egypt
Intel Labs Egypt
Intel Labs Egypt
Abstract—In this paper we discuss the potential effects of
using various segment durations of the available contents within
a simulated mobile environment. OPNET modeler was used to
implement a real-time test platform for mobile video streaming
using OPNET system in the loop component (SITL). The realtime test platform was then used to evaluate the performance of
the requests for different segment size. Dynamic Adaptive
Streaming over HTTP (DASH) protocol was used to provide
different test cases for video content with different segment sizes.
In our experiment, we used different segments of different sizes
(4, 8, and 12 sec.). Our comparison criteria include client
download throughput (Bandwidth utilization), and CPU
consumption. We identify the different trade-offs to be made in
order to optimize the usage of available resources.
Keywords—Video processing, DASH, dynamic segment size,
streaming, adaptation, segment duration, user experience,
Media streaming is one of the most prominent applications
over the internet, and is trending to become more important
than social media . Due to its importance, various studies
have focused around how to enhance the streaming user
experience, especially over mobile devices [2-4]. A main focus
was the adaptation to the surrounding environmental
conditions, that was later named as Dynamic Adaptive
Streaming (DAS), or to be more focused on HTTP streaming,
Dynamic Adaptive Streaming over HTTP (DASH) .
Previous research has been focused on dynamic adaptation
considering changes in bandwidth, or other context related
information to adhere to the environment , . One of the
points that has been discussed is how content and player
(streamer) settings can be manipulated to adapt with mobile
environments . In , the authors have tested briefly the
effect of video chunk on the streaming quality of experience
within a pre-defined mobile environment. In this paper, we
have extend this work.
In this paper, we evaluate the performance of video
streaming over a simulated mobile WiMAX network using
different segment duration while observing the throughput
(bandwidth utilization) and CPU consumption at the player
side. We have implemented a HTTP adaptive player where the
logic adapts to the estimated available bandwidth. We have
chosen to use OPNET  as our simulation environment. Open
source content has been used and modified as part of our
The paper is organized as follows. Section II highlights
related work to support our choices in the field and the
simulation model. This is followed by detailed explanation of
the experimental setup in Section III. We detail the outcome of
our experiments in the results in Section IV, followed by
summary of conclusions and potential future work.
This section entails information about related work to our
field of interest. We start with a background introduction about
streaming over HTTP, followed by an introduction about
DASH. We then give details about WiMAX structure, and why
we have chosen it as the core of our simulation. We then
discuss previous research that was potentially discussing
changing the content structure for better adaptation.
A. Media streaming over HTTP
Streaming is the act of consuming (displaying) the content
without entirely downloading the media . HTTP became
the standard protocol for media streaming on the internet as it is
platform independent, and the internet became a common
available resource .
B. Dynamic adaptive streaming over HTTP
As was mentioned, the usage of media streaming
applications is exceeding even social media communication
. Mobile users have also increased over the past decade, and
are expected to grow up to 33 times over the next decade .
Due to the enormous change in the availability of resources, a
new research trend started based on adapting the media stream
according to the available resource. This new trend, called
dynamic adaptive streaming over HTTP (DASH) focuses on
adapting while considering trade-offs of various resources such
as: bandwidth, timing, and quality .
2. DASH has been standardized by 3GPP, with certain
specification of its structure . The specification is divided
player over various segment durations, as well as studying the
CPU consumption at the client side.
Contents are stored at different bitrate levels
Each representation includes a list of video segments
that can be requested by the video client.
It is also possible to include a list of video servers
(baseURL) where the content (segments) are stored.
All of this information is stored in a single Media
Presentation Description (MPD) file which the client
downloads and parses during streaming. The MPD file
structure is shown in Figure 1.
OPNET modeler was used to simulate the WiMAX
network. Physical devices were connected to the simulated
network to provide a realistic test platform using the OPNET
System-in-the-loop (SITL) module. An open-sourced content
“Big Buck bunny” was used for streaming. The content was
split over different durations. The durations tested where 4, 8,
and 12-second segments.
As shown in Figure 2, the simulation platform consists of
OPNET simulator (on a laptop) that simulates the wireless
A physical streaming server connected to OPNET using a
SITL component assigned to a network interface card
A physical video client, with a video streaming player
supporting DASH, also connected to the OPNET
simulated network through a dedicated SITL module
assigned to a different NIC.
OPNET was used to simulate one mobile video client using the
trajectory shown in Figure 2. The video client receives the
streamed video after it passes through the OPNET simulated
network. The trajectory was chosen such that the mobile user
moves gradually near to or far from the WiMAX base station in
order to study the client mobility effect on the received video
quality. The mobility of the client was moving at a constant
speed of 40 km/hr.
Figure 1. DASH MPD file structure
C. Analysis of streaming over mobile enviornments
Research has been focused lately on studying the effect of
various adaptive streaming techniques over mobile
environments. For example, there has been an empirical study
comparing various streaming protocols under mobile
Since our focus is on the effect of adapting the segment size
to the mobile environment, in this section we mention the
related work done in this area.
In , segment sizing has been done based on the
popularity of the segment, where larger segment size is used
for more popular segments to lower the power consumed
during multiple requests. In , another technique was done
over TCP protocol that changes the segment size depending on
the previous measured error rate, where they try to minimize
the segment size if they get a higher frame rate error.
An empirical study has been focused on viewing the
different effects of mobility over media streaming regarding
the quality of experience of the video. However the focus on
the effect of the chunk size was limited to the effect on
glitches appearance and buffering . In our work, we focus
more on the throughput (bandwidth) consumption of the client
Video Streaming Server
Figure 2. Real-time wireless video streaming platform showing the mobile
After running the experiment, we analyzed the resulting
statistics regarding two aspects: 1- Throughput (bits/sec), and
2- CPU consumption.
We want to visualize the effect of changing segment size on
the actual throughput received by the client. We measure
throughput from the OPNET simulation side. Figure 3
represents the results we got from the simulation over segments
with sizes 4s, 8s and 12s.
a) The durability of the streaming (how long the stream
was able to hold up with changes happening at the mobile
trajectory): We noticed that the shorter the segments’
duration, the longer it was able to stream.
b) Frequency of requests: although the durability of the
streaming is more persistant in shorter segments, the frequency
of data transfere over HTTP is increased with using shorter
segment durations. This means that the amount of time spent
consuming other resources such as power and CPU on the
client side is greater. We will discuss that in more details in
the CPU consumption analysis.
B. CPU Consumption
Another factor that we are considering is the percentage of
CPU consumed by the streaming application through the
requests made by different segment sizes. Measuring the CPU
consumption has been done using the Windows Performance
(a) Segment size = 4 Seconds
(b) Segment size = 8 Seconds
(c) Segment size = 12 Seconds
Figure 3. Throughput (bits/sec)
From Figure 3, 2 key elements were observed as follows:
In order to unify the test, we have selected to detect the
results over the first 25 seconds of streaming. This is to
eliminate the consequences resulting from the video becoming
incapable of streaming over larger segment durations as the
video gets trapped in a loop of several attempts re-requests.
Therefore, it consumes un-utilized computational power. Our
goal is to focus on the power consumed during the actual
streaming. We have also made sure that we unify the
environment through fixing all the applications running in
parallel with the player.
In Figure 4, we noticed that the trend average CPU
consumption overtime is lower in the 8 seconds duration
content than in the 4 seconds, which is expected as we
mentioned in the previous section that we expect lower
frequency of requests, and therefore lower computational
consumption overtime. However we notice that in the 12
seconds, the CPU consumption average gets higher. Still, the
peak CPU consumption is higher at lower segment durations.
We related this to the fact that although the frequency of
requests have decreased, the internal processing done within
the player for re-allocation of memory space and rendering
increases if the segment duration exceeds the processing
capabilities of the player in use.
(a) Segment size = 4 Seconds
Different wireless network (LTE for example)
By varying such parameters, we can get more detailed
description for the factors that affect mobile streaming
efficiency which in turn, will allow us to provide robust
Another area of potential future work is devising an
adaptation logic that better adapts to the mobility conditions
discussed in the paper and enhance the overall streaming
(b) Segment size = 8 Seconds
(c) Segment size = 12 Seconds
Figure 4. CPU consumption percentage of overall CPU consumption
In this paper, we presented a platform for mobile video
streaming over WiMAX network using OPNET modeler. We
discussed the effect of changing segment duration over DASH
streaming within the simulated mobile environment by
streaming contents at different segment sizes.
By analyzing the throughput and the CPU consumption of
the client player, it was observed that smaller segment sizes
have resulted in smoother streaming with limited buffering
events or video cutoff during streaming. In addition, large
segment sizes consume more power. We concluded that proper
segment size selection during mobile video streaming can
potentially improve video streaming quality.
As a continuation to this analysis, more factors can be
discussed in the future, for example
The effect of duration change over the actual
content quality requested.
Other segment sizes
Different mobile speed
Different mobile trajectories
Different network conditions
The effect of the changing the segment duration
over the usage of the graphics device interface
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