WO2015100560A1 - Procede pour predire la qualite d'experience d'un service video mobile, et station de base - Google Patents

Procede pour predire la qualite d'experience d'un service video mobile, et station de base Download PDF

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Publication number
WO2015100560A1
WO2015100560A1 PCT/CN2013/090962 CN2013090962W WO2015100560A1 WO 2015100560 A1 WO2015100560 A1 WO 2015100560A1 CN 2013090962 W CN2013090962 W CN 2013090962W WO 2015100560 A1 WO2015100560 A1 WO 2015100560A1
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WO
WIPO (PCT)
Prior art keywords
epsnr
base station
video
prediction model
evaluated
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Application number
PCT/CN2013/090962
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English (en)
Chinese (zh)
Inventor
陈亮
韩广林
费泽松
白伟
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华为技术有限公司
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Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to CN201380003135.7A priority Critical patent/CN105264907B/zh
Priority to PCT/CN2013/090962 priority patent/WO2015100560A1/fr
Publication of WO2015100560A1 publication Critical patent/WO2015100560A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N17/00—Diagnosis, testing or measuring for television systems or their details
    • H04N17/004—Diagnosis, testing or measuring for television systems or their details for digital television systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/24—Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth, upstream requests
    • H04N21/2402—Monitoring of the downstream path of the transmission network, e.g. bandwidth available

Definitions

  • the embodiments of the present invention relate to the field of mobile communications, and in particular, to an experience quality prediction method and a base station for a mobile video service. Background technique
  • MOS Mean Opinion Score
  • the QoE of a video is predicted by means of secondary mapping. Specifically, some parameters of the Radio Access Network (RAN), such as a Signal to Interference Plus Noise Ratio (SINR), a delay, and a user resource, are mapped to obtain a packet loss rate, a bandwidth, and the like. Discrete, objective metrics, then map the discrete, objective metrics to the QoE of the video.
  • RAN Radio Access Network
  • SINR Signal to Interference Plus Noise Ratio
  • the embodiment of the invention provides a method for predicting the quality of a mobile video service and a base station, which can accurately predict the QoE of the video service by directly predicting the QoE of the video service according to the RAN side parameter.
  • an embodiment of the present invention provides a method for predicting an experience quality of a mobile video service, including: The base station acquires a radio access network RAN side parameter of the video user to be evaluated, and the RAN side parameter includes: the signal to interference and noise ratio SINR of the video user to be evaluated, in a cell serving the video user to be evaluated Number of video users NU, the time delay T of the video user to be evaluated on the security gateway interface SGi;
  • the base station determines an enhanced subjective test score eMOS of the video user to be evaluated according to the ePSNR and the enhanced subjective test score eMOS prediction model.
  • the base station determines, according to the RAN side parameter and the enhanced peak signal to noise ratio ePSNR prediction model, before the enhanced peak signal to noise ratio ePSNR of the video user to be evaluated , Also includes:
  • NU(T + b) describes a set of parameters in which the sample video user obtains the ePSNR according to the ePSNR prediction model that is most correlated with the actual ePSNR of the sample video user.
  • the base station according to the ePSNR and the enhanced subjective test score eMOS prediction model, Before determining the enhanced subjective test score eMOS of the video user to be evaluated, it also includes:
  • the subjective test score MOS was obtained by a linear regression fit.
  • an embodiment of the present invention provides a base station, including:
  • An obtaining module configured to obtain a radio access network RAN side parameter of a video user that needs to be evaluated,
  • the RAN side parameter includes: a signal to interference and noise ratio SINR of the video user to be evaluated, and a number NU of video users in a cell serving the video user to be evaluated, where the video user to be evaluated is Delay T on the security gateway interface SGi;
  • a first determining module configured to determine an enhanced peak signal to noise ratio ePSNR of the video user to be evaluated according to the RAN side parameter and the enhanced peak signal to noise ratio ePSNR prediction model acquired by the acquiring module;
  • a second determining module configured to determine, according to the ePSNR determined by the first determining module and the enhanced subjective test score eMOS prediction model, an enhanced main observation score eMOS of the video user to be evaluated.
  • the base station further includes:
  • the base station further includes:
  • the subjective test score MOS of the sample video user is obtained by linear regression fitting.
  • an embodiment of the present invention provides a base station, including: a processor and a memory, where the memory stores an execution instruction, when the base station is running, between the processor and the memory In the communication, the processor executes the execution instruction, and obtains a radio access network RAN side parameter of the video user to be evaluated, where the RAN side parameter includes: the signal to interference and noise ratio SINR of the video user to be evaluated, The number of video users in the cell to be served by the video user to be evaluated, NU, the time delay T of the video user to be evaluated on the security gateway interface SGi;
  • An enhanced subjective test score eMOS of the video user to be evaluated is determined based on the ePSNR and the enhanced subjective test score eMOS prediction model.
  • NU(T + b) The set of parameters of the ePSNR obtained by the ePSNR prediction model that is most correlated with the actual ePSNR of the sample video user.
  • MOS is subjected to a linear regression fit.
  • the method for predicting the quality of the mobile video service and the base station are provided by the embodiment of the present invention.
  • the base station After obtaining the RAN side parameter of the video user to be evaluated, the base station directly maps the RAN side parameter to the ePSNR of the video user, and then determines according to the eMOS prediction model. EMOS, to determine the need The QoE of the video user to be evaluated.
  • the base station only maps the RAN side parameters of the video users that need to be evaluated once, so that the ePSNR of the video user QoE is obtained, and a more accurate prediction of the video service QoE is realized.
  • 1 is a flowchart of Embodiment 1 of an experience quality prediction method for a mobile video service according to the present invention
  • FIG. 2 is a schematic structural diagram of a HAS video according to the present invention
  • FIG. 3 is a schematic diagram of a transmission process of a HAS video according to the present invention.
  • Embodiment 4 is a fitting curve diagram of a subjective test MOS score and SINR in Embodiment 2 of an experience quality prediction method for a mobile video service according to the present invention
  • FIG. 5 is a fitting curve diagram of a subjective test MOS score and a number of cell users in the second embodiment of the method for predicting the quality of the mobile video service according to the present invention
  • FIG. 6 is a fitting curve diagram of a subjective test MOS score and a time delay T in the second embodiment of the method for predicting the quality of the mobile video service according to the present invention
  • Embodiment 7 is a schematic structural diagram of Embodiment 1 of a base station according to the present invention.
  • Embodiment 8 is a schematic structural diagram of Embodiment 2 of a base station according to the present invention.
  • FIG. 9 is a schematic structural diagram of Embodiment 3 of a base station according to the present invention. detailed description
  • FIG. 1 is a flowchart of Embodiment 1 of an experience quality prediction method for a mobile video service according to the present invention.
  • the execution entity of this embodiment is a base station, and is applicable to a scenario in which accurate prediction of the video service QoE is required. Specifically, the embodiment includes the following steps:
  • the base station acquires a radio access network RAN side parameter of the video user to be evaluated, and the RAN side parameter includes: a signal to interference and noise ratio SINR of the video user to be evaluated, and a video user in a cell serving the video user to be evaluated.
  • Number NU video users to be evaluated at the security gateway interface The delay on the SGi.
  • the RAN side factors that the base station needs to consider are: the SINR of the video user to be evaluated, the number of cell users, that is, the number of video users in the cell serving the video user to be evaluated, and the video user who needs to be evaluated are in security.
  • the base station can obtain the SINR of the video user to be evaluated through the reporting mechanism of the user equipment (User Equipment, UE), and at the same time, since the base station grasps the information of all the subordinate cells, the base station can obtain the needs assessment.
  • the base station determines an enhanced peak signal to noise ratio ePSNR of the video user to be evaluated according to the RAN side parameter and the enhanced peak signal to noise ratio ePSNR prediction model.
  • PSNR is an objective standard for image quality evaluation. Therefore, the method of objective evaluation of the image can be applied to the video service, for example, the PSNR of the video user can be obtained by secondary mapping in the prior art.
  • the PSNR obtained by the RAN side parameter mapping in the embodiment of the present invention is referred to as the enhanced peak signal noise in comparison with the PSNR obtained by the secondary mapping in the prior art.
  • the subjective test score obtained from the ePSNR, and then based on the ePSNR, is called the enhanced subjective test score eMOS o
  • the base station determines the ePSNR of the video user to be evaluated according to the obtained RAN side parameter and the ePSNR prediction model.
  • the ePSNR prediction model may be obtained by fitting the subjective test score of the sample video user to the RAN parameter of the sample video, for example, the video user in the same or similar network environment as the video user to be evaluated, ie
  • the sample video user is, for example, the same system configuration as the network in which the video user to be evaluated is located.
  • the subjective test score for each sample video user is the exact value obtained by the person scoring the sample video.
  • the base station determines an enhanced subjective test score eMOS of the video user to be evaluated according to the ePSNR and the enhanced subjective test score eMOS prediction model.
  • the base station can determine the enhanced subjective test score eMOS of the video user to be evaluated according to the ePSNR and the enhanced subjective test score eMOS prediction model.
  • the eMOS prediction model can be obtained by linear regression fitting the sample video user's ePSNR and the subject video test subject MOS of the sample video user. For example, A number of video users are provided as sample video users by Next Generation Mobile Netwoks (NGMN), and ePSNR of the sample video users is determined according to step 102, and subjective tests are known to the sample video users. The score is line-followed with the determined ePSNR to determine the eMOS prediction model.
  • NVMN Next Generation Mobile Netwoks
  • the method for predicting the quality of the mobile video service provided by the embodiment of the present invention, after obtaining the RAN side parameter of the video user to be evaluated, directly mapping the RAN side parameter to the ePSNR of the video user, and then determining the eMOS according to the eMOS prediction model. Thereby determining the QoE of the video user that needs to be evaluated.
  • the base station only maps the RAN side parameters of the video users that need to be evaluated once, and then obtains the ePSNR of the video user QoE, thereby achieving a more accurate prediction of the video service QoE.
  • sample video user is used as an HTTP Adaptive Streaming (HAS) video as an example.
  • HTTP Adaptive Streaming HAS
  • ePSNR prediction model and the eMOS prediction model according to the sample video user and system configuration in the first embodiment of the present invention. Detailed instructions are given.
  • the HAS video service encodes a complete source video into several video at different bit rates, and segments the video at each bit rate, for example, requesting video segments of the corresponding code rate based on current channel conditions.
  • the HAS video may be a M3U8 format source video having M code rates and each code rate is divided into N segments, where M is the maximum code rate.
  • the sender generates a corresponding M3U8 file for each bit rate video, and the file includes a Uniform Resource Locator (URL address) of each segment of the corresponding code rate.
  • URL address Uniform Resource Locator
  • the sender generates a total M3U8 file, and the total M3U8 file stores the address of the M3U8 file corresponding to each bit rate.
  • the receiving end Before the video is played, the receiving end first downloads the main M3U8 file and the M3U8 file corresponding to each bit rate, and then downloads the first video segment for playing.
  • FIG. 3 is a schematic diagram of a transmission process of a HAS video according to the present invention.
  • the video is divided into 4 segments as an example.
  • the first code rate as shown by the slash fill in the figure
  • the second code rate as shown in the square fill in the figure
  • the receiving side sends an HTTP acquisition request (HTTP GET) to the sender, and the sender sends data to the receiver through the network, S ⁇ HAS video.
  • HTTP GET HTTP acquisition request
  • S ⁇ HAS video S ⁇ HAS video.
  • the sender cannot send the entire source HAS video to the receiver, but only the requested segments (Request Segments), that is, the first code rate as shown in the figure.
  • the first segment, the second, fourth, and fifth segments of the second code rate and the third segment of the third code rate are transmitted to the receiver.
  • the HAS video received by the receiver is damaged.
  • a, b, c, and d are a set of parameters that make the correlation between the ePSNR obtained by the sample video user according to the ePSNR prediction model and the actual ePSNR of the sample video user.
  • NMMN Next Generation Mobile Netwoks
  • the actual ePSNR is determined according to a conventional method, and subjective test scores and samples of the sample video users are determined.
  • ePSNR e X ePSNR +f (2) .
  • e 1.41og 2 ( face + 6 ⁇ 71 ) + 6.70 ( 5 ) ;
  • the base station acquires the RAN side parameter of the video user to be evaluated, that is, the SINR, the number of video users in the cell serving the video user to be evaluated NU, and the video user to be evaluated at the security gateway interface
  • the delay T on the SGi can accurately estimate the QoE based on the three parameters and the formula (6).
  • the correctness of the experience quality prediction method for the mobile video service provided by the embodiment of the present invention is determined by the correlation coefficient obtained by fitting the RAN side parameter with the subjective test score.
  • FIG. 4 is a fitting curve diagram of subjective test MOS score and SINR in the second embodiment of the experience quality prediction method for the mobile video service according to the present invention.
  • the number of video users in the cell serving the video user to be evaluated is NU
  • the delay T of the video user to be evaluated on the security gateway interface SGi is 20 ms.
  • the abscissa is the SINR
  • the ordinate is the subjective test MOS score.
  • the linear correlation coefficient between the experimental data and the solid line that is, the Pearson correlation coefficient (PCC) is 0.9571. It can be seen that QoE can be accurately evaluated according to formula (6).
  • FIG. 5 is a subjective test in the second embodiment of the method for predicting the quality of the mobile video service according to the present invention
  • the SINR of the video user to be evaluated is specifically 7 dB
  • the delay T of the video user to be evaluated on the security gateway interface SGi is specifically 20 ms
  • the abscissa is the number of cell users, that is, the required
  • the estimated number of video users in the cell served by the video user is NU
  • the ordinate is the subjective test MOS score
  • the linear correlation coefficient between the experimental data and the solid line that is, the Pearson correlation coefficient (PCC) is 0.9535. It can be seen that QoE can be accurately evaluated according to formula (6).
  • FIG. 6 is a fitting curve diagram of subjective test MOS score and time delay T in the second embodiment of the method for predicting the quality of the mobile video service according to the present invention.
  • the number of video users in the cell serving the video user to be evaluated is NU
  • the SINR of the video user to be evaluated is specifically 7 dB
  • the abscissa is the delay T.
  • the ordinate is the subjective test MOS score
  • the linear correlation coefficient between the experimental data and the solid line that is, the Pearson correlation coefficient (PCC) is 0.9504. It can be seen that QoE can be accurately evaluated according to formula (6).
  • FIG. 7 is a schematic structural diagram of Embodiment 1 of a base station according to the present invention.
  • the base station provided in this embodiment is an apparatus embodiment corresponding to the embodiment of FIG. 1 of the present invention, and the specific implementation process is not described herein again.
  • the base station 100 provided in this embodiment specifically includes:
  • the obtaining module 11 is configured to obtain a radio access network RAN side parameter of the video user to be evaluated,
  • the RAN side parameters include: the signal to interference and noise ratio SINR of the video user to be evaluated, the number of video users in the cell serving the video user to be evaluated NU, and the delay of the video user to be evaluated on the security gateway interface SGi T;
  • the first determining module 12 is configured to determine, according to the RAN side parameter and the enhanced peak signal to noise ratio ePSNR prediction model acquired by the obtaining module 11, the enhanced peak signal to noise ratio ePSNR of the video user to be evaluated;
  • the second determining module 13 is configured to determine an enhanced subjective test score of the video user to be evaluated according to the ePSNR determined by the first determining module 12 and the enhanced subjective test score eMOS prediction model.
  • the base station acquires the RAN side parameter of the video user that needs to be evaluated. Then, the RAN side parameter is directly mapped to the video user's ePSNR, and then the eMOS is determined according to the eMOS prediction model, thereby determining the QoE of the video user to be evaluated.
  • the base station only maps the RAN side parameters of the video users that need to be evaluated once, and then obtains the ePSNR of the video user QoE, thereby achieving a more accurate prediction of the video service QoE.
  • FIG. 8 is a schematic structural diagram of Embodiment 2 of a base station according to the present invention. As shown in FIG. 8, the base station 200 of this embodiment is further based on the structure of the apparatus of FIG. 9, and further includes:
  • NU(T + b) is a set of parameters that make the ePSNR obtained by the sample video user according to the ePSNR prediction model the most relevant to the actual ePSNR of the sample video user.
  • FIG. 9 is a schematic structural diagram of Embodiment 3 of a base station according to the present invention.
  • the base station 300 provided in this embodiment includes: at least one bus 31, at least one processor 32 connected to the bus 31, and at least one memory 33 connected to the bus 31, wherein the processor 32 passes through the bus 31.
  • the code stored in the memory 33 is called to: obtain the radio access network RAN side parameter of the video user to be evaluated, and the RAN side parameter includes: a signal to interference and noise ratio SINR of the video user to be evaluated, which is a video user that needs to be evaluated.
  • NU(T + b) b, c, d are a set of parameters that make the ePSNR obtained by the sample video user according to the ePSNR prediction model the most relevant to the actual ePSNR of the sample video user.
  • the user's subjective test score MOS is obtained by a linear regression fit.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Les modes et formes de réalisation de la présente invention se réfèrent à un procédé permettant de prédire la qualité d'expérience d'un service vidéo mobile, et à une station de base. Le procédé consiste à : faire obtenir, par une station de base, un paramètre de réseau d'accès radio (RAN) d'un utilisateur vidéo à évaluer ; déterminer la valeur de crête du rapport signal/bruit renforcé (ePSNR) de l'utilisateur vidéo à évaluer selon le paramètre de RAN et un modèle de prédiction de valeur de crête de rapport signal/bruit renforcé (ePSNR) ; et déterminer une note moyenne d'opinion renforcée (eMOS) de l'utilisateur vidéo à évaluer selon l'ePSNR et un modèle de prédiction de note moyenne d'opinion renforcée (eMOS). Dans le procédé, la station de base peut obtenir une ePSNR indiquant la QoE d'un utilisateur vidéo en ne mappant qu'une seule fois un paramètre de RAN de l'utilisateur vidéo à évaluer, afin de prédire précisément la QoE d'un service vidéo.
PCT/CN2013/090962 2013-12-30 2013-12-30 Procede pour predire la qualite d'experience d'un service video mobile, et station de base WO2015100560A1 (fr)

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CN201380003135.7A CN105264907B (zh) 2013-12-30 2013-12-30 移动视频业务的体验质量预测方法及基站
PCT/CN2013/090962 WO2015100560A1 (fr) 2013-12-30 2013-12-30 Procede pour predire la qualite d'experience d'un service video mobile, et station de base

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CN106998322A (zh) * 2017-02-20 2017-08-01 南京邮电大学 一种使用视频业务的平均意见分均值特征的流分类方法
WO2023051318A1 (fr) * 2021-09-28 2023-04-06 中兴通讯股份有限公司 Procédé de formation de modèle, procédé de planification de ressources sans fil et appareil associé, et dispositif électronique

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CN107733705B (zh) * 2017-10-10 2021-01-15 锐捷网络股份有限公司 一种用户体验质量评估模型建立方法及设备
CN109921941B (zh) * 2019-03-18 2021-09-17 腾讯科技（深圳）有限公司 网络业务质量评估和优化方法、装置、介质及电子设备
CN112383828B (zh) * 2019-12-12 2023-04-25 致讯科技(天津)有限公司 一种具有类脑特性的体验质量预测方法、设备及系统
CN112636976B (zh) * 2020-12-23 2022-11-22 武汉船舶通信研究所（中国船舶重工集团公司第七二二研究所） 业务质量确定方法、装置、电子设备和存储介质

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WO2023051318A1 (fr) * 2021-09-28 2023-04-06 中兴通讯股份有限公司 Procédé de formation de modèle, procédé de planification de ressources sans fil et appareil associé, et dispositif électronique

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