CN109905382B - Subjective and objective comprehensive evaluation method for user experience quality of IPTV video stream service - Google Patents

Subjective and objective comprehensive evaluation method for user experience quality of IPTV video stream service Download PDF

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CN109905382B
CN109905382B CN201910118920.6A CN201910118920A CN109905382B CN 109905382 B CN109905382 B CN 109905382B CN 201910118920 A CN201910118920 A CN 201910118920A CN 109905382 B CN109905382 B CN 109905382B
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魏昕
赵家林
高赟
唐菁
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a comprehensive evaluation method for subjective and objective experience quality of IPTV video stream service users, which comprises the following steps: the method comprises the following steps: acquiring QoS parameters and state parameters generated when an IPTV user watches videos; step two: processing the collected QoS parameters by using a data mining technology, and determining key QoS parameters in the IPTV video service; acquiring two personalized parameters, namely a watching rate and a watching mode, which respectively represent the user preference degree and influence the user acceptance degree according to the state parameters; step three: and bringing the processed key QoS parameters and the personalized parameters into a user experience quality evaluation model obtained based on a statistical method to obtain a user experience quality score. The evaluation model containing the personalized parameters provided by the invention can enable the obtained evaluation result to be closer to the real user experience, improves the accuracy of QoE evaluation of IPTV video streaming service, and can help operators to better improve service.

Description

Subjective and objective comprehensive evaluation method for user experience quality of IPTV video stream service
Technical Field
The invention relates to the field of user experience evaluation of video streaming services, in particular to an objective and comprehensive evaluation method for user experience quality of IPTV video streaming services.
Background
With the continuous development and application of internet technology, people have higher and higher requirements on multimedia service quality. Meanwhile, various multimedia services, such as an interactive network television (IPTV), a voice over internet protocol (VolP), etc., are increasingly emerging. The IPTV integrates various technologies such as Internet, multimedia, communication and the like, and has high sensitivity to network damage such as packet loss, network jitter, transmission delay and the like. Therefore, mobile IPTV service operators must establish a sophisticated video Quality of service (QoS) and end-user Quality of Experience (QoE) evaluation system. Particularly, compared with the traditional QoS, the QoE considers the subjective factors of the user, can better reflect the real feeling of the user, is beneficial to an operator to grasp the customer demand, improves the service quality and improves the market competitiveness.
However, since QoE is subjective, the most accurate measurement method is to perform subjective video quality assessment, but this requires careful selection of users to perform in a tightly controlled environment, which is too costly. In this case, more and more scholars start to evaluate the quality of experience, i.e., QoE, of the user using QoS parameters. However, this method lacks subjective factors, and there are problems that the degree of correlation between the obtained evaluation value and the user's true feeling is not high and the prediction accuracy is low.
Disclosure of Invention
The invention aims to provide an IPTV video stream service user experience quality subjective and objective comprehensive assessment method which can better reflect the subjective feeling of a user and can accurately assess the experience quality of each user.
In order to achieve the purpose, the invention adopts the following technical scheme: the method for objectively and comprehensively evaluating the experience quality of the IPTV video stream service user comprises the following steps:
the method comprises the following steps: acquiring QoS parameters and state parameters generated when an IPTV user watches videos;
step two: processing the collected QoS parameters by using a data mining technology, and determining key QoS parameters in the IPTV video service; acquiring two personalized parameters, namely a watching rate and a watching mode, which respectively represent the user preference degree and influence the user acceptance degree according to the state parameters;
step three: and calculating the influence function value of the key QoS parameter on the user experience quality and the influence function value of the personalized parameter on the user experience quality, and bringing the influence function values into a user experience quality evaluation model obtained based on a statistical method to obtain a user experience quality score.
Further, the method for comprehensively evaluating the user experience quality of the IPTV video streaming service objectively includes: in step one, the collected QoS parameters specifically include:
time delay: the time from the user request to the final establishment of the user request;
dithering: the user receives a time interval between two adjacent data packets;
media packet loss rate: indicating the transmission packet loss rate of the tested video stream;
packet loss rate: the ratio of the number of lost packets to the number of transmitted packets;
switching time: the time required by the user to switch channels;
the number of requests: the user initiates the request times;
failure times are as follows: the number of times of failure of user initiation request;
availability ratio: the ratio of available bandwidth to total bandwidth;
maximum jitter: maximum jitter in the user viewing process;
the acquired state parameters specifically include:
start time: the time at which the user starts to watch the program;
end time: the time when the user stops watching the program;
program duration: the total duration of the program viewed by the user;
the signal transmission mode: including unicast and multicast;
and (3) a viewing mode: including channel, time shift, and on demand.
Further, the method for comprehensively evaluating the user experience quality of the IPTV video streaming service objectively includes: the second step specifically comprises:
(2.1) substituting the QoS parameters collected in the step one into a screening formula using a Pearson correlation coefficient as an evaluation index to obtain key QoS parameters, wherein the key QoS parameters specifically comprise: delay, media packet loss rate, maximum jitter and availability; the screening formula is as follows:
Figure BDA0001970158920000031
Figure BDA0001970158920000032
Kqos＝If(|r|＞0.1)
in the above equation, r denotes the pearson correlation, N denotes the number of samples in the data set, Xi is the sample value,
Figure BDA0001970158920000033
is the sample mean value, σxIs the index standard deviation, σyStandard deviation of user experience quality score (MOS value), KqosFor the key QoS parameter, If (-) is a parameter which meets the input condition; wherein r is [ -1,1 [ ]]A medium value, 1 represents negative correlation, and 1 represents positive correlation;
(2.2) evaluating the user's favorite degree of a program by using the ratio Vr of the time of the user watching the program to the total time length of the program, wherein the expression of the watching rate Vr is as follows:
Figure BDA0001970158920000034
where end _ time represents the time when the user stops watching the program, start _ time represents the time when the user starts watching the program, program _ time represents the total duration of the program watched by the user, and VtRepresents a viewing rate;
(2.3) viewing mode K of user is signal transmission mode KtAnd a viewing mode KmJoint decisions include multicast-channel, unicast-time-shift, and unicast-on-demand.
Further, the method for comprehensively evaluating the user experience quality of the IPTV video streaming service objectively includes: the third step specifically comprises:
(3.1) calculating an influence function value fqos of the key QoS parameter on the user experience quality, wherein the expression of the influence function value fqos is as follows:
Figure BDA0001970158920000041
wherein, A is a parameter for adjusting QoE scale, and the value is 17; b is a control parameter, and the value is 5.1; c. C1,c2,c3,c4,c5Is a parameter for adjusting the sensitivity, and the values are respectively 0.005, 106, 0.01, 0.9 and-1.6;
(3.2) calculating an influence function value fper of the personalized parameter on the user experience quality, wherein the expression of the influence function value fper is as follows:
Figure BDA0001970158920000042
wherein, beta is a parameter for adjusting the sensitivity to the preference degree, and the value of beta is 0.71; when the user adopts a multicast-channel watching mode, K is 1.2; when the user adopts a unicast-channel viewing mode, K is 1.08; when the user adopts a unicast-time shift watching mode, K is 1.12; when the user adopts a unicast-on-demand watching mode, K is 1.41;
(3.3) bringing the influence function value fqos and the influence function value fper into a user experience quality evaluation model together, and calculating to obtain a user experience quality score, wherein the user experience quality evaluation model specifically comprises the following steps:
SMOS＝min(fqos×fper,5)
wherein S isMOSThe evaluation result is user experience quality score, the value range is 0-5, the larger the value is, the better the video quality is, min (-) is the minimum value in the input sequence, fqos is the influence function value, and fper is the influence function value.
Through the implementation of the technical scheme, the invention has the beneficial effects that:
(1) the invention utilizes the data mining technology, reduces the dimension of QoS on the premise of ensuring the accuracy, can effectively reduce the complexity of the model and save the computing resources;
(2) the invention not only uses the traditional QoS parameter to evaluate the user experience quality, but also adds two personalized parameters of the watching rate and the watching mode, which can better reflect the subjective feeling of the user, improve the accuracy of QoE evaluation of IPTV video stream service, and help the operator to better improve the service;
(3) according to the invention, an evaluation model is established according to the relation between each parameter and the user experience quality, and the value of each constant parameter is solved by using the least square method, so that the user experience quality can be accurately evaluated, the method has high accuracy, the accurate user experience can be fed back in time, and the operator is helped to continuously perfect the video service.
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Fig. 1 is a schematic flow chart of a comprehensive subjective and objective evaluation method for IPTV video streaming service user experience quality according to the present invention.
FIG. 2 is a diagram of the machine learning verification result of the present invention.
Fig. 3 is a diagram of a user experience quality prediction result according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
As shown in fig. 1, the method for comprehensively evaluating the user experience quality of the IPTV video streaming service objectively includes the following steps:
the method comprises the following steps: collecting parameters generated when an IPTV user watches videos, wherein the parameters comprise QoS parameters and state parameters;
the collected QoS parameters specifically include:
delay (Td): the time from the user request to the final establishment of the user request;
jutter (Jitter): the user receives a time interval between two adjacent data packets;
media packet loss rate (Pmlr): indicating the transmission packet loss rate of the tested video stream;
(iv) packet loss ratio (Pplr): the ratio of the number of lost packets to the number of transmitted packets;
switching time (Taat): the time required by the user to switch channels;
sixthly, number of requests (Nreq): the user initiates the request times;
number of failures (Nfail): the number of times of failure of user initiation request;
-availability (Pcur): the ratio of available bandwidth to total bandwidth;
ninthly maximum jitter (Mjitter): maximum jitter in the user viewing process;
wherein, the acquired state parameters specifically include:
starting time (start _ time): the time at which the user starts to watch the program;
end time (end _ time): the time when the user stops watching the program;
program time length (program _ time): the total duration of the program viewed by the user;
signal transmission mode (K)t): including unicast and multicast;
viewing mode (K)m): including channel, time shift, and on demand;
step two: processing the acquired parameters, and determining key QoS parameters which have large influence on user experience in the IPTV video service by using a data mining technology; obtaining two personalized parameters, namely a watching rate and a watching mode, which respectively represent the user's liking degree and influence the user's acceptance degree according to the state parameters, and specifically comprising the following steps: (2.1) substituting the QoS parameters collected in the step one into a screening formula using a Pearson correlation coefficient as an evaluation index to obtain key QoS parameters, wherein the key QoS parameters specifically comprise: the method comprises the following steps of time delay, media packet loss rate, maximum jitter and availability rate, wherein a screening formula is as follows:
Figure BDA0001970158920000061
Figure BDA0001970158920000062
Kqos＝If(|r|＞0.1)
in the above equation, r denotes the pearson correlation, N denotes the number of samples in the data set, Xi is the sample value,
Figure BDA0001970158920000072
is the sample mean value, σxIs the index standard deviation, σyIs the standard deviation of the MOS value, KqosFor the key QoS parameters, If (-) is the parameter that meets the input condition; wherein r is [ -1,1 [ ]]A medium value, 1 represents negative correlation, and 1 represents positive correlation;
(2.2) evaluating the user's favorite degree of a program by using the ratio Vr of the time of the user watching the program to the total time length of the program, wherein the expression of the watching rate Vr is as follows:
Figure BDA0001970158920000071
where end _ time represents the time when the user stops watching the program, start _ time represents the time when the user starts watching the program, program _ time represents the total duration of the program watched by the user, and VtRepresents a viewing rate;
(2.3) the receiving degree of the watched video by the user is influenced by the watching mode K of the user, namely, the same video is watched in different modes, and the experience quality of the user is different; the watching mode K of the user is a signal transmission mode KtAnd a viewing mode KmJoint decisions include multicast-channel, unicast-time-shift, and unicast-on-demand.
Step three: calculating an influence function value of the key QoS parameter on the user experience quality and an influence function value of the personalized parameter on the user experience quality, bringing the influence function values into a user experience quality evaluation model obtained based on a statistical method, and calculating an MOS value, namely a user experience quality score; the method specifically comprises the following steps:
(3.1) calculating an influence function value fqos of the key QoS parameter on the user experience quality; the key QoS parameters can be divided into two types, namely positive and negative, wherein the positive parameters include: availability ratio; the negative parameters are: delay, jitter, media packet loss rate, packet loss rate; when the value of the negative parameter becomes larger, the user experience becomes worse, and when the value of the positive parameter becomes larger, the user experience can be improved; the sensitivity of the user to the negative parameter is changed, the sensitivity of the user is reduced along with the continuous increase of the negative parameter value, and the user is hardly sensitive to the negative parameter any more when reaching a certain degree; the expression of the function fqos is as follows:
Figure BDA0001970158920000081
in the formula, A is a parameter for adjusting QoE scale, and the value is 17; b is a control parameter, and the value is 5.1; c. C1,c2,c3,c4,c5Is a parameter for adjusting the sensitivity, and the values are respectively 0.005, 106, 0.01, 0.9 and-1.6;
(3.2) calculating an influence function value fper of the personalized parameters on the user experience quality; the psychological state of the user is related to the preference degree of the user to the service and is negatively related, namely under the same QoS condition, when the user has preference to the service, the expectation degree of the user to the service is relatively higher, so that the satisfaction degree is reduced; the influence of the user preference degree on the QoE is not unlimited, namely the user experience only fluctuates within a certain range under the condition of certain QoS; the expression of the function fper is as follows:
Figure BDA0001970158920000082
wherein, beta is a parameter for adjusting the sensitivity to the preference degree, and the value of beta is 0.71; when the user adopts a multicast-channel watching mode, K is 1.2; when the user adopts a unicast-channel viewing mode, K is 1.08; when the user adopts a unicast-time shift watching mode, K is 1.12; when the user adopts a unicast-on-demand watching mode, K is 1.41;
(3.3) bringing the influence function value fqos and the influence function value fper into a user experience quality evaluation model together, and calculating to obtain a user experience quality score, wherein the user experience quality evaluation model specifically comprises the following steps:
SMOS＝min(fqos×fper,5)
wherein S isMOSIs to evaluateThe value range of the obtained user experience quality score (MOS value) is 0-5, the video quality is better when the value is larger, min (-) is the minimum value in the input sequence, fqos is an influence function value, and fper is an influence function value.
Performance evaluation:
the invention carries on the experiment according to the flow shown in fig. 1, extracts the QoS parameter and the status parameter, and makes the experimental sample data through the actual data collected by the operator; in this experiment, the evaluation indices are MAE and goodness of fit.
FIG. 2 shows the evaluation results before and after adding personalized parameters using various machine learning algorithms; from the results, it can be found that the MOS value can be well mapped only by using the QoS parameter, but after the personalized parameter is added, the root mean square error between the predicted value and the true value is reduced by about 19%, and the prediction effect is better, so that the personalized parameter is effective and necessary in evaluating the QoE.
FIG. 3 shows the prediction results of the present invention, from which it can be seen that the predicted values are substantially evenly distributed on both sides of the subjective quality assessment results, with a goodness of fit of 0.828; compared with the traditional QoS/QoE mapping model, the method has better prediction performance, can achieve better prediction precision, and can help operators to better improve services.
The invention has the advantages that:
(1) the invention utilizes the data mining technology, reduces the dimension of QoS on the premise of ensuring the accuracy, can effectively reduce the complexity of the model and save the computing resources;
(2) the invention not only uses the traditional QoS parameter to evaluate the user experience quality, but also adds two personalized parameters of the watching rate and the watching mode, which can better reflect the subjective feeling of the user, improve the accuracy of QoE evaluation of IPTV video stream service, and help the operator to better improve the service;
(3) according to the invention, an evaluation model is established according to the relation between each parameter and the user experience quality, and the value of each constant parameter is solved by using the least square method, so that the user experience quality can be accurately evaluated, the method has high accuracy, the accurate user experience can be fed back in time, and the operator is helped to continuously perfect the video service.

Claims (1)

  1. An objective comprehensive evaluation method for user experience quality of IPTV video stream service is characterized in that: the method comprises the following steps:
    the method comprises the following steps: acquiring QoS parameters and state parameters generated when an IPTV user watches videos;
    in step one, the collected QoS parameters specifically include:
    time delay: the time from the user request to the final establishment of the user request;
    dithering: the user receives a time interval between two adjacent data packets;
    media packet loss rate: indicating the transmission packet loss rate of the tested video stream;
    packet loss rate: the ratio of the number of lost packets to the number of transmitted packets;
    switching time: the time required by the user to switch channels;
    the number of requests: the user initiates the request times;
    failure times are as follows: the number of times of failure of user initiation request;
    availability ratio: the ratio of available bandwidth to total bandwidth;
    maximum jitter: maximum jitter in the user viewing process;
    the acquired state parameters specifically include:
    start time: the time at which the user starts to watch the program;
    end time: the time when the user stops watching the program;
    program duration: the total duration of the program viewed by the user;
    the signal transmission mode: including unicast and multicast;
    and (3) a viewing mode: including channel, time shift, and on demand;
    step two: processing the collected QoS parameters by using a data mining technology, and determining key QoS parameters in the IPTV video service; acquiring two personalized parameters, namely a watching rate and a watching mode, which respectively represent the user preference degree and influence the user acceptance degree according to the state parameters;
    the second step specifically comprises:
    (2.1) substituting the QoS parameters collected in the step one into a screening formula using a Pearson correlation coefficient as an evaluation index to obtain key QoS parameters, wherein the key QoS parameters specifically comprise: delay, media packet loss rate, maximum jitter and availability; the screening formula is as follows:
    Figure FDA0003032602570000021
    Figure FDA0003032602570000022
    Kqos＝If(|r|＞0.1)
    in the above equation, r represents the pearson correlation, N represents the number of samples in the data set, Xi is the sample value,
    Figure FDA0003032602570000023
    is the sample mean value, σxIs the index standard deviation, σyStandard deviation of user experience quality score (MOS value), KqosFor the key QoS parameter, If (-) is a parameter which meets the input condition; y isiRefers to the collected real experience value fed back by the ith IPTV user,
    Figure FDA0003032602570000024
    refers to the average of the true experience values of all user feedback, where r is [ -1,1 [)]The median value, -1 represents negative correlation, and 1 represents positive correlation;
    (2.2) evaluating the user's favorite degree of a program by using the ratio Vr of the time of the user watching the program to the total time length of the program, wherein the expression of the watching rate Vr is as follows:
    Figure FDA0003032602570000025
    wherein end _ time represents the time when the user stops watching the program, start _ time represents the time when the user starts watching the program, and program _ time represents the total duration of the program watched by the user;
    (2.3) viewing mode K of user is signal transmission mode KtAnd a viewing mode KmCo-decisions including multicast-channel, unicast-time-shift, and unicast-on-demand;
    step three: calculating an influence function value of the key QoS parameter on the user experience quality and an influence function value of the personalized parameter on the user experience quality, and bringing the influence function values into a user experience quality evaluation model obtained based on a statistical method to obtain a user experience quality score;
    the third step specifically comprises:
    (3.1) calculating an influence function value fqos of the key QoS parameter on the user experience quality, wherein the expression of the influence function value fqos is as follows:
    Figure FDA0003032602570000031
    wherein, A is a parameter for adjusting QoE scale, and the value is 17; b is a control parameter, and the value is 5.1; c. C1,c2,c3,c4,c5Is a parameter for adjusting the sensitivity, and the values are respectively 0.005, 106, 0.01, 0.9 and-1.6; t isdRepresents the time delay, PmlrIndicating media packet loss rate, PplrIndicates packet loss rate, MjitterIndicates the maximum jitter sum PcurRepresenting an availability rate;
    (3.2) calculating an influence function value fper of the personalized parameter on the user experience quality, wherein the expression of the influence function value fper is as follows:
    Figure FDA0003032602570000032
    wherein, beta is a parameter for adjusting the sensitivity to the preference degree, and the value of beta is 0.71; when the user adopts a multicast-channel watching mode, K is 1.2; when the user adopts a unicast-channel viewing mode, K is 1.08; when the user adopts a unicast-time shift watching mode, K is 1.12; when the user adopts a unicast-on-demand watching mode, K is 1.41;
    (3.3) bringing the influence function value fqos and the influence function value fper into a user experience quality evaluation model together, and calculating to obtain a user experience quality score, wherein the user experience quality evaluation model specifically comprises the following steps:
    SMOS＝min(fqos×fper,5)
    wherein S isMOSThe evaluation result is user experience quality score, the value range is 0-5, the larger the value is, the better the video quality is, min (-) is the minimum value in the input sequence, fqos is the influence function value, and fper is the influence function value.
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