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A Buffer-Aware HTTP Live
Streaming Approach for
SDN-Enabled 5G Wireless
Networks
Speaker: Bo-Yu Huang
Advisor: Dr. Ho-Ting Wu
Date: 2015/04/15
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Outline
 Introduction
 Multimedia Streaming for SDN-enabled 5G Wireless Networks
 Buffer-Aware HTTP Live Streaming Mechanism
 Analysis of implementation results
 Conclusion
 Reference
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Introduction
 5G communication technology will be able to achieve some challenging
requirements. Thus it makes the performance of traditional network
architecture increasingly unable to keep up with 5G communication.
Therefore, software-defined networks (SDNs) were regarded as a
revolutionary technology to subvert the traditional networking industry.
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Introduction(Cont.)
 Currently, people tend to watch movies, videos, and TV programs via
streaming servers, resulting in the discussion of various subjects.
 Hence the adaptive streaming technologies were proposed in the past
that can dynamically select video content based on current network
conditions or mathematical capabilities of hardware in order to provide
higher-quality streaming service to users.
 However, in heterogeneous networks some studies have attempted to
improve the bandwidth prediction for effective transmission rates
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Introduction(Cont.)
 Different from other studies on 5G networks, this study aims at a dynamic
adjustment video streaming mechanism with HTTP live streaming protocol
according to the utilization and stability of the routers and switches of SDN
and the network condition of 5G, enhancing service quality.
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Multimedia Streaming for SDN-enabled
5G Wireless Networks
 SDN presents a software layer to make the network device adjustments
through SDN defined. Therefore, there are two planes in SDN network
devices: the control plane and the data plane.
 Because of this separation, network administrators are no longer executing
all the control rules on the physical network devices individually.
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Multimedia Streaming for SDN-enabled
5G Wireless Networks(Cont.)
 However, multimedia streaming, which is one of the most bandwidthconsuming services, is an urgent challenge, and researchers have been
designing new architectures and mechanisms for providing a multimedia
streaming mechanism on SDN-enabled 5G wireless networks. That is the
motivation for the buffer-aware HTTP live streaming approach proposed in
this study for SDN-enabled 5G wireless networks, as shown in Fig. 1.
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Buffer-Aware HTTP Live Streaming
Mechanism
 This study proposes a dynamic adaptive streaming mechanism, based on
quality of service (QoS) by researching the buffer status of UEs buffer, and
aims to make a dynamic adjustment according to the 5G wireless
communication network.
 The streaming mechanism, preloads media segments under quality
permission, where better quality media segments are downloaded on the
local side for buffering. This mode is called the buffering mode.
 If the network condition does not allow better buffering, the media
segment quality is lower than the threshold and buffering is abandoned,
and segments most suitable for the bandwidth of the time are adjusted
and selected for real-time streaming according to the network prediction.
This mode is called the real-time streaming mode.
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Buffer-Aware HTTP Live Streaming
Mechanism(Cont.)
 The initialization is executed after the UE starts, and the index file is analyzed
in this stage, where the analyzed resolution and quantification parameters
are entered to obtain the score value to determine the lowest-level media
information that can be preloaded in the play list. This level is called the
basic level.
 Afterward, the system enters the preloading stage, the basic level segment
is preloaded for t seconds, and the upper limit of buffering for preloading is
6t second playing time.
 The size is obtained from
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Buffering Mode
 This mode aims to preload media segments of better quality into the buffer
of the local side so the user will not receive content of worse quality.
 There are two dynamic adaptation strategies in buffering mode:
1. scale up mode
2. scale down mode
 In order to effectively evaluate the heterogeneous network conditions and
adjust the quality of the media segment to be buffered in this mode, this
study proposes a combination of a buffer-based bandwidth forecasting
method and level equation for the dynamic adaptive adjustment strategy
in buffering mode.
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Buffer Bandwidth Forecasting Method
 This method is designed by observing the change in the stream playing
length stored in the buffer. This approach mainly detects the changes
when there is large growth in the network condition.
 In terms of method design and implementation, the number of
downloaded segments is recorded once per second, five consecutive
data packets are multiplied by the segment playing length, and
calculated by the least square method to determine slope m.
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Scale Up Mode
 In scale up, when the buffer bandwidth forecasting method detects the
network condition becoming increasingly better, the media segment of
better quality is downloaded.
 The precondition of this strategy is that the available playing time exceeds
2t seconds.
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Scale Down Mode
 The scale down mode is used when the network condition is poor and the
balance between download and consumption has failed. In order to
continuously provide the user with picture quality above the basic level, the
level is corrected down in order to maintain buffering mode as possible
 When the average buffer time is shorter than the threshold, the
downloaded media level is scaled down. The precondition of scale down is
that the slope is less than zero.
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Buffer-Aware HTTP Live Streaming
Mechanism(Cont.)
 When scale up or scale down is executed, it will enter into the idle mode
within five seconds, and without any adjustment. As the recording
continues, the number of occupied buffers, or the downloaded quantity, is
recorded once per second.
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Level Equation
 The level equation aims to calculate the level difference between other
representations recorded in index and the current streaming download.
The difference refers to different bit rates. The influential factors discussed in
this article are resolution and quantification parameters. According to :
 The two equations are combined to calculate the gap of the current
bitrate, expressed as
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Level Equation(Cont.)
 The Gt calculated by Eqs. 4 and 5 refers to the bit rate gap between two
representations, and according to the bandwidth increasing trend, as
calculated from the level difference and buffer bandwidth forecasted by
scale up and scale down, the level equation of Eq. 6 is defined,
 The level equation determines integers between –2 and 4, where the level
differences calculated by the aforesaid two equations may be equal to
each other, meaning there may be multiple representations equal to the
level difference of the media segment under streaming download.
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Level Equation(Cont.)
 If the scale up or scale down mechanism determines the level, then the
choice best meeting economic benefit is calculated, namely, the sum of
the scores of the representation, as derived from the score graph, is divided
by Gt, where a higher ratio means better quality can be obtained by lower
bandwidth, expressed as
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Level Equation(Cont.)
 The flow chart of buffering mode streaming mechanism in the system
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Real-time Streaming Mode
 This streaming mode is designed for poor network conditions. In buffering
mode, when the UE has exhausted the media segments temporarily stored
in the local side, the system enters into streaming mode and abandons
buffering.
 The downloaded quantity per second, as recorded in the scale down state,
is used for polynomial regression. Five pieces of historical information are
used for two-dimensional polynomial regression to calculate the estimated
bandwidth at the next second.
 The polynomial regression is defined as:
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Real-time Streaming Mode(Cont.)
 A two-dimensional polynomial regression curve is deduced from the
collected data cluster, which is used for forecasting at the next time point,
as shown in Fig. 4.
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Buffer-Aware HTTP Live Streaming
Mechanism(Cont.)
 After initializing the streaming service, the SDN controller directly presets the
UE into buffering mode, and changes into real-time streaming mode when
the number of buffered media segments is reduced to 0.
 When buffering mode changes into real-time streaming mode, the
downloading task is directly abandoned, and the media segment closest
to the bandwidth, as calculated by polynomial regression, is downloaded
until the ratio of total playing time of downloaded basic level media
segments to the download time is higher than 1.
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Analysis of implementation results
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Analysis of implementation results
(Cont.)
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Analysis of implementation results
(Cont.)
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Analysis of implementation results
(Cont.)
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Conclusion
 In this study, a buffer-aware HTTP live streaming approach for SDN-enable
5G wireless networks was proposed, and the two modes were adopted for
controlling the streaming quality based on the router management of the
SDN controller and the bandwidth prediction of 5G. It makes the most
suitable quality for the network environment to be determined.
 Finally, the results prove that the mechanism could maintain a certain level
of streaming quality for SDN-enabled 5G wireless networks and ensure
smooth and complete streaming services.
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Reference
 Chin-Feng Lai, Ren-Hung Hwang, Han-Chieh Chao, Mohammad Mehedi
Hassan, Atif Alamri, “A buffer-aware HTTP live streaming approach for SDNenabled 5G wireless networks,” Network, IEEE, Vol. 29, no. 1, Jan.-Feb. 2015,
pp. 49–55.