article

1. EEIC’2019 University of Bejaia
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The impact of jitter on the HEVC video streaming with
Multiple Coding
Farouk BOUMEHREZ A.Hakim SAHOUR Noureddine DOGHMANE
Department of Electronics, Department of Electronics, Department of Electronics,
Abbes Laghrour University, Abbes Laghrour University, Badji Mokhtar University,
Laboratoire des Télécommunications (LT) Route de Constantine, Khenchela 40000, Algeria BP 12, Sidi Amar, Annaba, Algeria.
8 Mai 1945 University, Guelma, Algeria. Laboratoire d'Automatique et Signaux
d'Annaba (LASA), Annaba, Algeria.
boumfarouk@yahoo.fr hakim-sahour1@yahoo.fr ndoghmane123@yahoo.fr
Abstract— In this paper, we assessed the new and emerging
video coding standard HEVC/H.265 from the viewpoint of
quantization parameter (QP) impact, the video content, and
the degradation brought about by the transmission channel on
the quality of the experience (QoE). Also, a study of the quality
of service (QoS) and QoE that will allow us to evaluate
multimedia applications in wireless Ad-Hoc Networks is
proposed.
The main contribution of this paper is the performance
evaluation of the codec HEVC/H.265 based on the QP
variation values for different video content. This can be used to
reduce their impacts on video transmission through a
corrupted wireless channel. In this work, we also studied the
effects of jitter on the QoE of the transmitted video sequences.
The results prove that the use of the QP value can recover the
jitter impacts and ameliorate the received video
quality. Finally, an HEVC encoder parameter adaptation
scheme based on MDC (Multiple Description Coding) is
modeled with the encoder parameter and QoE model. A
comparative study shows that the proposed MDC technique is
efficient for better transmission.
Keywords— HEVC/H.265; Multiple Description Coding (MDC);
Quantization Parameter (QP); Jitter, Video content.
1. I. INTRODUCTION
Nowadays, video transmission over all kinds of
networks is a field that has kept growing in the last years.
the improvements in network properties together with a
larger efficiency of video codecs and an increase in
processing ability and storing capacity of all kinds of
devices (desktop computers, smart-phones, tablet
computers, ...) help to make video streaming feasible. Lots
of applications use video streaming [1][2].
Digital uncompressed videos contain large amounts of data.
This is the reason why video codecs plays an important role,
as they are able to compress video sequences with large
frame sizes and frame rates into relatively small bit-streams
while keeping the quality of perception acceptable.
Video has been for a long time a very important media for
communications, and many other applications. Originally
the video is analog since its generation, using sensors, up to
its transmission via radio or cable and its reception. With the
evolution of techniques and digital technologies this led to
the digitization of this type of content .This digitization
allowed us to have high quality and high definition videos.
Only, this was accompanied by a huge quantity of digital
data to process, transmit or store. The solution adopted is of
course the compression. Video compression has emerged as
an area of research vital and much solicited from the end of
the 1980s.
The popularity of the internet from the mid-1990s
encouraged the digital video to be transmitting under IP.
Only, this diffusion under IP has been facing several
problems and handicaps [2][3]. Among these handicaps the
bandwidth of communication used under IP systems is
generally low and variable in time. Also, the principle of a
transmission under IP often favors losses of packets and
delays [4].
The term QoE is actually most used. QoE represents the set
of objective and subjective characteristics that satisfy, which
determinate the interaction between the user’s perception
(light intensity, color, etc.), and the video presented with
expression in words, such as bad, poor, fair, good or
excellent[2]. In contrast, the measurement of QoE is done
by a subjective assessment of a person or a consistent
population of users on a service they use. QoE is generally
used alongside QoS. Moreover, delay, jitter, PLR (Packet
Loss ratio), and bandwidth, are some of the most common
parameters used to measure QoS [5]. Now, the QoS
considered weakly in determining the quality, the term QoE
is dominant to represent it [6].
Indeed, jitter is one of the important parameters that
deteriorate the QoS. Jitter is the variation of delays, or the
difference in transmission delay of packets transmitted
between two systems of multimedia data communication
over a network.
Figure.1 IP Network with packet loss and Jitter.

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The HEVC (H.265) standard is the successor of
H.264/AVC (Advanced Video Coding), which was
developed similarly to H.264 by a joint effort between
ISO/IEC Moving Picture Experts Group, and ITU-T video
coding experts group (VCEG). The main objective of the
new codec is to provide 50% better compression efficiency
than H.264 encoding[7], as well as support for display
resolutions up to 8192 X 4320 with better network
transmission [6] [7].
Real-time video transmission over wireless channels is
a big defiance in the last few years. The transmission of the
raw video is impossible because of the large bandwidth
required, that's why the video compression is inevitable.
Moreover, compressed video is very sensitive to packet loss
delay, and jitter happening in best-effort networks like the
Internet.
MDC is an efficient method established to deal with the
transmission of data through disturbed networks [8]. It
encodes a signal in several bit-streams, every bit-stream is a
description, and every description is independently
decodable [8]. The received signal can be reconstructed with
several descriptions which give a quality improvement.
Indeed, MDC minimizes the impacts of QoE parameters by
transporting the descriptions with diversified paths. As well,
various error concealment techniques can be developed to
recapture the lost information [03] [8]. The advantages of
the MDC are obtained to the detriment of additional
redundancy in the descriptions. Thus, one of the principal
objectives of the MDC technique is to minimize redundancy
[8]. In the temporal domain MDC category; mainly the
frames are divided between the descriptions; the odd frames
in one description and the even frames in the other
description. On the decoder side, decoding the lost frames
are replaced by frames freezing or estimated by concealment
methods [8] [15]. The motion estimation/compensation is
performed intra description, which means that even (odd)
frames are predicted from odd (even) frames.
In [10][11] the authors explain the impact of video
content and transmission impairments on QoE of a MPEG-2
(Moving Picture Experts Group-2) video stream transmitted
through a wired network. The MPEG-2 video standard is
involved and found that the loss and the jitter parameters
have a significant influence on the user experience, and the
impact of codec and network settings on QoE is dependent
on the video content.
Previous works have also addressed studies employing
the codec H.264/AVC, using subjective and objective
evaluation methods. In [5], the video sequences encoded
using the H.264/AVC and transmitted via a typical noisy
wireless channel with transmission interruption, the results
have been saved and analyzed.
In [6] it examines the impact of video content types and
the encoding parameters based on QP in HEVC video
quality, with objective and subjective tests. Therefore, the
obtained results have shown that encoding parameters and
the variety of video content types affect the video quality.
In [3], the author has proposed the use of MDC
methods for video compression through the use of Multiple
Description, a novel system was presented, whose main idea
was the use of H.264/AVC based on MDC for wireless
video transmission.
The authors of [8] demonstrated that the temporal MDC
scheme is a simple alternative for video streaming where
methods that use error control schemes such as Forward
Error Correction (FEC) or Automatic Repeat Request
(ARQ) are not suitable for transmission impairments. Thus,
they evaluate the performance of the proposed MDC
image/video coder for two descriptions.
The rest of this paper is organized as follows: Section II
describes the system block diagram. Section III introduces
the HEVC encoder parameter model. Section VI describes
the system tested on wireless IP transmission solutions. The
objective QoE prediction model for HEVC encoded video
streaming. Section V discusses the collected results. Finally,
the conclusion of the paper is found in section VI.
II. SYSTEM DESCRIPTION
This paper includes three parts, i.e. Part A: HEVC encoder
parameter model, Part B: objective QoE prediction model
and Part C: HEVC encoder adaptation scheme. The overall
system block diagram is shown in Fig 2.
(a)
(b)
Figure.2 Adaptation Scheme for objective assessment of video
sequence: (a) Without MDC and (b) with MDC.
Our contributions can be outlined as:
A) Analyze the impact of video content (characterized by
motion activities and complexity of video sequences) and
encoding parameters (QP, resolution, frame rate...) on the
new and emerging standard of video coding HEVC.
B) Concentrate specifically on source level protection, such
as MDC as an approach which is fighting the loss of video
packets and delay variation. The proposed model discusses
the reliable transmission issues of video HEVC/H.265 over
wireless communication environments. Objective video
quality evaluation according to QoE prediction model in
wireless IP network scenarios.

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III. HEVC ENCODER PARAMETER MODEL
The defined video content type is used with different
QP values to model the impact of the HEVC encoding
process for a given test scenario. The video sequences have
been coded using the full range QP (17, 32 and 42) as
recommended by JCT-VC [12]. The conditions under which
HEVC should be tested [7] are chosen to obtain different
objective metrics such as PSNR.
A. Experiment Settings
The JCT-VC defines many encoder configurations [7]
in the reference software HM 11.0[12], such as all intra
mode (AI), random access mode (RA), and low delay mode
(LD) [7] [12].In this experiment, each frame in the test
video sequences is coded in the Low delay with B slices
(LB) because it’s suitable for live streaming and video
conferencing applications [7].
The input data is six standard test video sequences.
They had 4CIF spatial resolutions were used from the
ReTRiEVED video quality Database [10] [11]. The source
videos had different characteristics like context, motion,
temperature, color and camera movements, etc. The
uncompressed video sequence format is YUV4:2:0, and
represented as 8 bits per sample. Table 1 presents the
characteristics of standard test video sequences obtained for
experiments and sample frames extracted from each video
sequence are shown in Fig.3. Figure.4 shows the spatial
perceptual information (SI) and temporal perceptual
information (TI) planes as recommended by ITU [10], it can
be noticed that the SI and TI indexes vary from relatively
small to relatively large values for the selected content.
Table 1 Original video sequences include resolution, frame rate (fps),
length and frames.
Videos
Resolution
(Pixel)
FR
(fps)
Length
(s)
Frames
Duckstakeoff 704 x 576 25 9 250
Ice 704 x 576 30 7 240
Crowdrun 704 x 576 25 9 250
Harbour 704 x 576 30 9 300
Soccer 704 x 576 30 7 300
Parkjoy 704 x 576 25 8 250
In this paper, video quality evaluation is divided into
three steps,
1. The video should be encoded.
2. We are streaming with FFmpeg to the destination via
a disturbed channel accumulated by the network emulator
(NetEM) [10].
3. Stream the video from the transmitter to the receiver
by a wireless LAN architecture configured at the ''ad-hoc''
mode. The used computer is HP laptop with a CPU Intel (R)
Core (TM) i7-4790S, Processor Speed @3. 20GHZ, RAM 8
GO, system type 64-bit, processor architecture running
Ubuntu 14.04LTS Linux. Moreover, the distance between
the two computers is 100 m. The emulation setup is as in
Fig.5.
Figure.3 Sample frames from considering videos.
Figure.4 TI and SI planes for raw videos.
Figure.5 Proposed Testbed System.
The effect of jitter has been added by introducing a
fixed delay of 100 ms plus five variable delays (1, 2, 3 4,
and 5 ms), the selection of Jitter values are based on the ITU
recommendation [11]. On the receiver side, a VLC player
was used.
Therefore, to study the effect of the jitter, the video
content, and the quality perception, the impact jitter
variations at a time was considered, i.e., when study de
jitter effect, we didn't take into account the impact of the
delay, packet loss and bandwidth limitation.
In this paper, we have used principally the objective
metrics, Thus, PSNR is an objective quality assessment
most used; it performs a pixel by pixel comparison between
the reference and the deformed content [6].
PSNR is defined using the mean square error (MSE)
𝑀𝑆𝐸 =
1
𝑚𝑛
[𝐼 𝑖, 𝑗 − 𝑘(𝑖, 𝑗)]2
𝑛− 1
𝑗 =0
, (1
𝑚−1
𝑖=0
)

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𝑃𝑆𝑁𝑅 = 10 𝑙𝑜𝑔10
𝑀𝐴𝑋𝑙
2
𝑀𝑆𝐸
𝑑𝐵 (2)
Where, MAX is the maximum value of the pixel which can
take (for example, 255 for the 8-bit image).
This criterion provides the error between the original video
and the reconstructed video; a higher PSNR usually
indicates better quality.
B- HEVC Encoder Parameter Model without MDC
By applying the QP values 17, 32 and 42, we extract the
corresponding PSNR from the encoding process. The
obtained results are presented in Fig. 6, for the test video
sequences with HEVC encoder.
Figure.6 Impact of QP on HEVC Model without MDC.
All obtained results demonstrated that when encoding
different videos with the same codec parameters such as QP,
frame rate, and spatial resolution, produced different PSNR
values as shown in Fig. 6. Moreover, as the QP increases the
bitrate reduces resulting in a degradation in video quality,
thing proved in several studies [6][10]. On the contrary, the
lowest QP values result in higher bitrates, which leads to
increased video quality. As the PSNR varies with different
video sequences, it can be asserted that the content type has
an impact on PSNR values; this indicates that other
parameters must be influencing in the PSNR values than the
encoding parameters.
C- HEVC Encoder Parameter Model based on MDC
By MATLAB language tool, two descriptions (sub-
streams) of the same video are created; these two latter’s
take the even frames and the odd frames respectively. Then,
in the same conditions mentioned earlier, the HEVC /H.265
encoder has been applied to each description. On the
decoder side, the central description is reconstructed from
the bit-streams with HEVC/H.265 decoder. Generally, each
side decoding correlated with side distortion, while the
decoding of all descriptions is called central decoding with
the smallest central distortion [8]. Figure.7 shows the graph
of the PSNR, according to the QP.
Figure.7 Impact of QP on HEVC Model based on MDC
From the Fig. 7, it can be observed that the PSNR decreases
gradually with the increased of QP values such as indicated
in the previously obtained results as shown in Fig. 6. Also,
the PSNR takes the same downtrend in both cases. It can be
noted that when QP increases, there is some slight
difference between them that can be considered negligible.
This behavior was found in other studies [3][8]. Also, we
can see that the PSNR is positively correlated with video
quality as shown in Figs. 6 and 7.
The video sequences with the lowest PSNR presented
in both cases are respectively the Duckstakeoff, Parkjoy,
and Crowdrun. The PSNR of video sequences Ice, Soccer,
and Harbour are rather high, and then it seems that there is a
big variation between Crowdrun sequence and Ice sequence.
Thus, all encoded videos contain various content types
(motion activities (TI) and complexity of video (SI)) [6] as
shown in Fig. 4. Additionally, it should be noted the video
quality declining with the increase in QP value
(compression). However, it seems clear in the Figs.6 and 7
that the drop is at most intensity whenever the video has
higher motion activities compared to the video which has a
lower motion. Also, you should keep in mind the level of
compression because it determines the video quality.
Furthermore, all sequences need a different compression
level for the same quality in motion characteristics. Thus,
Ice, Soccer and Harbour video sequences require lower
compression and the transmission bandwidth requirement
for the same quality when compared to Duckstakeoff,
Parkjoy, and Crowdrun video sequences. Therefore, this is
important because it will enable us to know the QP effect
and how it should be used in designing an improved video
quality without disturbance model. Also, the frame rate has
an impact on the encoded video, in the same QP, the video
quality increases as the frame rate increases. As a result,
encoded and designed video quality in our prediction model
depends on HEVC/H.265 video codec, video content type
of video sequences as also indicated by authors in [4] and
[6] and the QP as the initial encoding settings determines the
initial video quality.

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VI- OBJECTIVE QOE PREDICTION MODEL FOR
HEVC ENCODED VIDEO STREAMING
A- Packet loss and Jitter impact on HEVC Video Streaming
Previous researches have demonstrated that the most
popular network impairment are jitter and PLR [5][10][11].
Thus, to study the video quality assessment; it has used the
HEVC encoder settings are discussed in Sec.III, the video
sequences which are presented in table 1. Then, it’s
transmitted over an emulated wireless IP-network as shown
in Fig.5. However, the Fig.8 shows that the smallest jitter
values give higher values of PSNR, indicating the better use
of the video perception. As jitter values increases the PSNR
drops steeply, but it remains acceptable until the values of
the jitter are superior to 2ms, after that the video sequence
will be degraded. Therefore, if videos with high values of
jitter were provided as a service, users might consider
stopping watching. The videos are more sensitive to jitter
[5][6][11]. It is proved that jitter can deteriorate the video
quality and results in a severe degradation in the perceived
video quality.
Figure.8 Impact of Jitter on perceived quality without MDC
Scheme.
It is evident that when the jitter values are smaller than 1ms,
there are significant differences in PSNR values for all
video sequences. Also, we have noticed that, if the video is
affected so much by jitter, the perceived quality associated
depends on the HEVC encoding process. Moreover, the
results of the impact of them are seemed clear on the graph.
Consequently, HEVC codec has a significant impact on the
compression rate and the visual quality of videos compared
to H.264 [5], and MPEG-2 [10][11].
B- Jitter impact on HEVC Video Streaming based on MDC
In this section, we have proposed a QoE model to use the
HEVC encoder adaptation scheme based on MDC technique
as shown in Fig. 2. Figure 9 presented the results in terms of
PSNR when applied the more robust temporal domain MDC
approach using HM11.0 and transmitted over the emulated
wireless IP-network. Then, on the decoder side, the video
sequences are reconstructed from odd/even frames. In the
same way, Figs.10 and 11 show that the variations of PSNR
with jitter are dropping trend similar from the most of the
cases.
Figure.9 Impact of Jitter on perceived quality with MDC scheme.
V- DISCUSSION
From the Fig 9, it can be seen that the perceived quality
degrades significantly in all considered videos for high
values of jitter [5][6][10] associated with a significant
improvement compared to HEVC encoder without MDC
scheme, as shown in Fig.8.
In addition, we have found that the PSNR decreases
gradually by increasing the QP values. This, with a
significant variation depending on video content types
which have different characteristics like motion level,
context, temperature, color and camera movements[10][11],
as indicated below (see Figs. 8 and 9), the video sequences
Ice, Soccer, and Harbour, are more resistant to jitter than
Duckstakeoff, Parkjoy, and Crowdrun video sequences.
Furthermore, the QP has a significant impact on the video
quality and compression rate; it regulates how much spatial
detail is maintained [6]. For all video content types, there is
a decrease in video quality when QP and jitter values
increase. For instance, there is a relationship between QP,
jitter and the quality degradation. Thus, the jitter impact on
video quality can be considered a factor of video quality
assessment as the QP (Bitrate), content Type, etc.
In this paper, we have demonstrated that when the Jitter
increases, it can minimize the impairment impact on a video
quality metric by a QP increasing (reducing the encoding
bitrate) (see also Figs. 8 and 9).
Moreover, the choice of a QP value in the HEVC
encoder represents an essential thing for the bitrate control
and the impairment impact on a video quality, as in the older
video compression techniques, e.g., H.264/AVC [5][13][14]
and MPEG-2[10][11].
Also, this study has shown that for the smallest jitter
values (1ms, and 2ms), the PSNR with or without the MDC
remains the same, as shown in Figs. 8 and 9, as expected
from theoretical results [3][8]. It can be seen that for higher
values, the MDC scheme leads to better results, indicating
that the MDC method is a solution used to counter the
higher values of PLR and Jitter.
To prove the MDC importance, we compared the impact
of Jitter on HEVC Video Streaming based on MDC scheme
with the impact of Jitter on HEVC Video Streaming without
MDC scheme. For this purpose, the obtained results can
help us to achieve a comparison between the obtained
averages PSNR of each QP value for all videos in both
cases. Moreover, it has been found that there are higher
values of PSNR in the presence of the MDC when jitter is
increasing because it’s a technique for combating the packet
losses as detailed in [8], i.e., it’s one of the big advantages
of this approach. It can be noted that the use of MDC is an
effective way to improve the transmission with a PSNR gain
1dB to 2dB for Jitter.
CONCLUSION
The use of multimedia services has become widespread
in the daily life. Video streaming with its various uses are
the typical example. The QoE is strongly recommended
0 1 2 3 4 5
10
20
30
40
50
Jitter (ms)
PSNR
[dB]
QP=17
0 1 2 3 4 5
10
20
30
40
50
Jitter (ms)
PSNR
[dB]
QP=32
0 1 2 3 4 5
10
20
30
40
50
Jitter (ms)
PSNR
[dB]
QP=42
Duckstakeoff
Parkjoy
Crowdrun
Ice
Soccer
Harbour
0 1 2 3 4 5
10
20
30
40
50
Jitter (ms)
PSNR
[dB]
QP=17
0 1 2 3 4 5
10
20
30
40
50
Jitter (ms)
PSNR
[dB]
QP=32
0 1 2 3 4 5
10
20
30
40
50
Jitter (ms)
PSNR
[dB]
QP=42
Duckstakeoff
Parkjoy
Crowdrun
Ice
Soccer
Harbour

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especially for certain types of networks like ad-hoc wireless
networks. Indeed, these networks have the particularity of
introducing even more errors disturbances and losses,
compared to the other types of networks essentially wired.
In this work, a simple MDC method is proposed to improve
the performance of the video quality; HEVC encoder
scheme is applied to encode and designed the end-to-end
video quality, which depends essentially on the video
content type and the QP as the initial encoding settings. This
method gives better results in terms of the PSNR. This paper
has demonstrated that when the Jitter increases, it can
minimize the video degradation in terms of video quality
metrics by the QP increasing. Thus, the comparative study
shows that for video transmission over wireless channels,
the MDC technique gives an improved solution for good
transmission.
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