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A New Approach Measuring Users' QoE in the IPTV

A New Approach Measuring Users' QoE in the IPTV

Bo Wang, Xiangmin Wen, Sun Yong, Zheng Wei

School of Information and Communication Engineering

Beijing University of Posts and Telecommunications

Beijing, China

Wangbo5629587@https://www.wendangku.net/doc/3214087455.html,, Xiangmw@https://www.wendangku.net/doc/3214087455.html,

Abstract—IPTV is a system where a digital television service is delivered by using IP packets over broadband network infrastructure. IPTV can be operated through telephone line that is installed almost in all houses. Transmitted video streams take bandwidth and also need priority running in queue with other services in the provider’s network. Such has in the effect implementation the quality of service (QoS) into network. QoS mechanisms allow the best way to transfer for different traffics through the network. Consumers want their IPTV programs to be smooth and visually excellent. From this perspective, it is specified as user’s quality of experience (QoE) that is the most important parameter for IPTV provider. Hence, network equipment must be rigorously tested that they fulfill QoE. In this paper, we present a new approach to estimate the users’ QoE, which uses the full reference model. We have implemented and evaluated a trail system, and the results show the relationship between users’ QoE and objective QoS.

Keywords- users’ QoE; full reference; video content; network transmission chatacteristics;key performance indexs

I.I NTRODUCTION

IPTV is a system where a digital television service is delivered by using IP packets over broadband network infrastructure. IPTV can be operated through telephone line that is installed almost in all houses. Transmitted video streams take bandwidth and also need priority running in queue with other services in the provider’s network. Such has in the effect implementation the quality of service (QoS) into network. QoS mechanisms allow the best way to transfer for different traffics through the network. Consumers want their IPTV programs to be smooth and visually excellent. From this perspective, it is specified as user’s quality of experience (QoE) that is the most important parameter for IPTV provider. Hence, network equipment must be rigorously tested that they fulfill QoE [1, 2].

QoE, which is the abbreviation of quality of experience, is an important parameter estimating the performance of device or networks from the view of user’s experience. QoE is different from previous professional parameters such as packet loss, delay and variation of jitter. Duing to good intuitive of result and easy to be understood by non-professionals, QoE has been widely recognized and researched.

Now, there are a lot of equipment manufacturers to provide IPTV system platforms and devices. The industrial recognized IPTV business includes two basic operations: TV live broadcast and VoD demand. TV live broadcast uses the technology of Multicast and VoD demand uses the technology of Unicast. From the perspective of users’ QoE, quality of IPTV is focused on two aspects: channel change time and estimation of video performance.

Channel change time or channel zapping time, generally, equals to Join Latency + CSD, but this assessment is inaccurate. The most accurate channel change time is I-Frame Join Latency. When a user’s request is trasmitted and I-Fram is recerved, it’s said that succuss to access to the channel and video into user. Estimation of video performance mainly has two methods: media deliver index (MDI) [3] and mean opinion score for video (MOS_V) [4, 5, 6]. Video quality is mainly impacted by the following features: video content (video film source itself or caused by encoder) and network transmission characteristics caused by injury.

In this paper, we present a new method to estimate the users’ QoE in the IPTV. In our method, we consider not only the relation between QoE and video content, but also the relation between QoE and network transmission characteristics (packet loss, delay and variation of jitter etc.). We adopt a way called MOS (Mean Opinion Score) evaluation of measurement, which we are familiar with in the voice test, to estimate the quality of video after it transmites on the network. The new method test and verify the video quality by using some KPIs (Key Performance Indictors) with the users’ vision system [7]. First, we use the source of video file as reference file, and compare the received video file with reference file. Second, we calculate the difference between the received video file and reference file from the perspective of users. Third, we calculate the different indicators and classify these indicators. Finally, Calculating the MOS by using the above analysis. Comparing with other methods, the main advantage of our new method is that we combine video content and network transmission to gain a comprehensive assessment of the quality of video streaming.

The remainder of this paper is structured as follows. In section II, we explore the existing technologies of users’ QoE. Our proposed method is discussed in section III. Section IV analyses the the test result and result the relation between users’ QoE and objective QoS parameters. Finally, we conclude this paper in Section V.

II.E XISTING T ECHNOLOGIES

Video quality assessment methods can be divided into three categories: Full-reference type (Full Reference), part of reference-based (Reduced Reference) and non-reference type (No Reference). The existing methods mainly belong to the non-reference type, such MDI and MOS_V.

2009 Pacific-Asia Conference on Circuits,Communications and System

Figure 1.

Full Reference

Figure 2.

Reduced Reference

Figure 3. Non-Reference

Figure 4. Intrusive Way

A. Full Reference

This is a "bilateral" approach, which compares the source of video with the received video file in the way of one frame by another in order to get the quality of video. The advantage of this approach is the video quality assessment of high accuracy. However, as the source and received videos one frame-by-comparison, it was not appropriate at the same time assess the multi-channel video streaming quality (usually limited to within 5). Therefore, it’s suitable for research institutions and codec equipment manufacturers to use the method of video quality to conduct in-depth analysis. The full reference type is shown in Fig. 1.

B. Reduced Reference

Actually, this is also a "bilateral" approach. However, it extracts the spatial and temporal info from the video source to compare with received video file. This method is rarely used relatively. Fig. 2 is the illustration of the reduced reference.

C. No Reference

This is a "unilateral" approach, the main factor used to assess a variety of network parameters impact on the video quality, which does not matter with the source video quality. The advantage of this video quality evaluation method is that it can be at the same time thousands of real-time analysis of the quality of streaming video, but the shortage is that it can not determine the quality of video source itself, good or bad. This method is suitable for equipment manufacturers and operators to assess the equipment or the whole system performance in the condition of knowing the quality of video source guaranteed. The non-reference type is shown in Fig. 3. MDI and MOS_V belong to this type.

Media Delivery Index (MDI) measurement can be used as a diagnostic tool or a quality indicator for monitoring a network intended to deliver applications such as streaming media, MPEG video, Voice over IP, or other

information sensitive to arrival time and packet loss. It provides an indication of traffic jitter, a measure of deviation from nominal flow rates, and a data loss at-a-glance measure for a particular flow. For instance, the MDI may be used as a reference in characterizing and comparing networks carrying UDP streaming media.

Regarding to the above three types of video quality assessment method, there are two ways used: one is intrusive and the other is non-intrusive.

Intrusive way is the realization of near-side location of the source of the video output of a reference signal in the receiving end of the corresponding video signal to give feedback and compared in order to achieve the assessment of video quality. This approach is mainly used in full-reference video quality assessment methods. Fig. 4 is the illustration of intrusive way.

The realization of non-intrusive way is that it does not achieve any reference signal in the source end, and only analyses the video quality in the receiver end, which is mainly used for no-reference video quality assessment.

III. O UR P ROPOSEED M ETHOD

In order to make the users’ QoE measurement more accurate, we consider more indicators which have impact on the users’ QoE. In our approach, we consider not only the network transmission characteristics caused by injury, but also the video content (video film source itself or caused by encoder).

A. The Indicator

The proposed approach is a video quality assessment method of the full-reference type and intrusive way. It uses a way called MOS (Mean Opinion Score) evaluation of measurement,which we are familiar with in the voice test, to estimate the quality of video after it transmites on the network. The new method tests and verifies the video quality by using some KPIs (Key Performance Indictors) with the users’ vision system.

Figure 5. The Principle of Our Proposed Method

There are two aspects which impact on the video quality: video content and network transmission. For the aspect of video content, there are three indicators:

? Blockiness – the damage caused by the low flow

rate and low-quality of the source code chip.

? Bluriness – the damage caused by low resolution

as a result of film source coding compression.

? Jerkiness – the damage caused by the network

damage and sampling frequency deviation largely. For the network transmission impacting video quality, there are four indicators:

? Frame freeze / skips - the damage caused by the

loss of video data (MPEG Frame).

? Noise – the damage caused by random noise and

transmission.

? Loss – a decline in video quality caused by the

severe packet loss.

? Jitter – the injury arising from wrong order or

error as a result of the inconsistent order of data packets to send and receive [10].

Comparing with other existing methods, the significant advantage of our new method is that we combine video content and network transmission to gain a comprehensive assessment of the quality of video streaming. In our method, the main indicators are as follows:

? MOS: This indicator classifies the video quality

into a 1-5 score: 1 for the worst, 5 for the best. This indicator is most clearly intuitive and most easily understood and accepted by users. It is the most important test indicator of our method. Other indicators mainly service for the MOS.

? Distortion Indicators: This indicator analyses the

various factors on the impact of video streaming in depth from the Luminance, chrominance and temporal domain.

? Delay: the time difference between the receiving

end video streaming data frame and source video frame.

? Brightness: the brightness value of the receiving

end video streaming and video streaming source. ? Contrast: the value of the order comparing the

receiving end video streaming data frame with the source of video frame.

? PSNR: Peak Signal-to-Noise Ratio, used to

analyze the received quality of video streaming. PSNR is divided into three: PSNR Y, PSNR Cb and PSNR Cr.

? Blur: the assessment of the outline of video

images for clarity and accuracy of three-dimensional space distortion.

? Blockiness: indicator of the damage caused by the lower stream rate and rough quantum of video images.

?

Frame Skips and Freezes: network overload results in the stagnation or video packet loss and other damage.

B. The Principle of Proposed Method

The proposed method consists of four parts. Fig. 5 shows the structure of our new method. 1) The first step: Pre-Processing Stage

In this phrase, we are going to achieve the alignment in the indicators of spatial and temporal between the source video file and the received video streaming. The main purpose of this step is to ensure that the source of video frames and received video frame can be compared with each other.

2) The second step: Calculating the perceptual difference of the aligned signals

Perceptual means one can only consider the perceived differences between parts of the video stream. Through the analysis of this step, we can get the perceived indicators of video streaming.

3) The third step: The classification of distortion parameters and indicators calculation

Through this step, we can calculate out all kinds of different factors between the received video streaming and video streaming sources. What’s more, we can make the classification of priority according to the factors impacting on the video quality.

4) The fourth step: Gaining the value of MOS

Through a comprehensive comparison and analysis of a variety of distortion factor and damage parameters, we can calculate out the results of the final MOS. MOS value is 1-5 from the scope of change. 1 is the representative of the quality of the worst, and 5 is the representative of the best quality. What’s more, MOS values are obtained at the same time, but also can be indicators to facilitate the depth analysis of the quality of video streaming.

IV. E VALUATION AND A NALYSIS

We can use the measuring workplace to measure our new method, as shown in Fig. 6.

The measuring workplace can simulate variety of situations in the IPTV network. The key device is NetSim that can change the quality of service (QoS) parameters in network, for example latency, packet loss, out-of-order packets. The device Ixload is set to terminate an Ethernet termination and simulates a set-top box and measures the our new method. The PC (IP address 10.1.1.1) is a control station for NetSim. PC includes also the video file in SDTV quality that is sent via VLC program on the network. Video stream can be sent by using two methods: unicast or multicast.

Due to the complexity of the network, there is no uniform requirement for the QoE indicators in the international arena. Through the experiment, we know that the network architecture design, video compression standard, video bit rate, the number of concurrent video streams and the buffer size of set-top boxes have the impact on the users’ QoE. We use a parameter to represent all kinds of factors related to the QoE, namely Objective

Figure 6. A simulation infrastructure of IPTV

Figure 7. The result of the laboratory testing

QoS. Fig. 7 shows the relationship between users’ QoE and Objective Qos.

From the data from laboratory testing, we conclude that the value of I-Frame Join Latency should be within 2 seconds, and the lowest value of MOS is more than 3.6, preferably more than 4.0. If less than 3.6, the video can not be accepted; higher than 4.0, it is a better video quality.

V.

C ONCLUSION

This paper presents a new method to estimate the users’ QoE in the IPTV, which is an approach of the full-reference type and intrusive way. Comparing with other existing technology, the significant advantage of our method is that we combine video content and network transmission to gain a comprehensive assessment of the quality of video streaming. We also get the illustration of user’s QoE and other parameters. What’s more, MOS values can be used as indicators to facilitate the depth analysis of the quality of video streaming. Based on these result, we are going to research the relationship between users’ QoE and Objective QoS and establish mathematical model in the future.

A CKNOWLEDGMENT

This work was supported by the Fund of National Nature Science of China(No. 60743007, 60872050).

R EFERENCES

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matlab课下作业

MATLAB 作业 1.构造矩阵A =[?718?3],B =[4257],C =[59 62 ] (1)试将A ·*B 与A*B 的结果相加,并找出相加后新矩阵中绝对值大于10的元素。 (2)组合成一个4×3的矩阵,第一列为按列顺序排列的A 矩阵元素,第二列为按列顺序排列的B 矩阵元素,第三列为按列顺序排列的C 矩阵元素。 解答: (1) 在matlab 上的程序如下: >> A=[-7,1;8,-3]; >> B=[4,2;5,7]; >> C=[5,9;6,2]; >> A.*B ans = -28 2 40 -21 >> A*B ans = -23 -7 17 -5 >> D=A.*B+A*B D = -51 -5 57 -26 >> D(find(D<-10|D>10)) ans = -51 57 -26 (2) 在matlab 上的程序如下: >> A=[-7,1;8,-3]; >> B=[4,2;5,7]; >> C=[5,9;6,2]; >> D=[A(:),B(:),C(:)] D = -7 4 5 8 5 6 1 2 9 -3 7 2 2、求解以下方程组的解(一组解即可)。

{4x +6y +3z =132x +3y +4z =9 解答: 在matlab 上的程序如下: >> A=[4,2;6,3;3,4]; >> A=A'; >> B=[13;9]; >> x=A\B x = 0 1.6667 1.0000 3、使用spdinag(B,d,m,n)命令创建4阶稀疏矩阵A,求其秩,并将A 转换为全矩阵C (其中矩阵B=[1;2;3;4],d=1)。 解答: 在matlab 上的程序如下: >> B=[1;2;3;4]; >> A=spdiags(B,1,4,4) A = (1,2) 2 (2,3) 3 (3,4) 4 >> sprank(A) ans = 3 >> full(A) ans = 0 2 0 0 0 0 3 0 0 0 0 4 0 0 0 0 4、求函数f (t )=1?12.5e ?t sin(2t +3.4)在t>0区间内的所有零点。 解答: 在matlab 上的程序如下: >> f=@(t)(1-12.5*exp(-t)*sin(2*t+3.4)); >> fplot(f,[0,10]); >> a=fzero(f,1) a = 1.6583 >> b=fzero(f,2) b = 1.6583 >> c=fzero(f,3) c =

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