无线视频流业务的用户体验质量估计模型及其应用
发布时间：2018-10-20 09:24
【摘要】：随着无线通信技术的高速发展,无线视频流业务应用越来越广泛,人们对无线视频流的服务质量期望也逐渐提高。为了获得用户对视频流服务的认可,服务提供商迫切需要建立一种以用户认可程度为标准的质量评价体系。传统的服务质量(Quality of Service, QoS)是一种被广泛采用的服务度量方法,但是QoS强调技术层面的客观评价指标,不能直接体现用户对视频流质量的真实感受。用户体验质量(Quality of Experience, QoE)是一种以用户认可程度为标准的服务度量方法,它以用户对业务的使用感受为研究重点,能评价业务中多种QOS指标对用户体验的影响。QoE直接反映了用户对服务的认可程度,是决定无线视频流业务能否取得成功的关键因素。因此,用户体验质量不仅是学术界的重点研究课题,也是工业界实现业务发展关注的焦点。为了保证无线网络视频流业务的用户体验质量,对无线视频流业务进行质量评估及其应用优化有重要的研究意义和实用价值。本文围绕无线视频流业务的QoE,研究无线网络中视频流业务的用户体验质量估计模型及其应用。 在无线视频流业务用户体验质量估计方面,针对现有QoE估计方法存在的评估指标不全面、评估准确度不理想等问题,本文提出了一种基于径向基函数神经网络(Radial Basis Function Neural Networks, RBFN)的无参考质量评估模型。首先,我们分析了端到端跨层参数对视频流业务用户体验质量产生的影响。然后,在无需原视频作比较的前提下,建立了基于RBFN的QoE估计模型,详细阐述了基于RBFN的QoE估计模型的评估原理与流程。最后,我们仿真验证所提出的QoE估计模型,并与其它四种典型的无参考估计模型进行比较分析,结果表明我们所提出的基于RBFN的QoE估计模型不仅评估准确度最高,而且具有低的时间复杂度。 针对QoE估计模型在无线视频流业务优化的应用方面,本文提出了一种基于QoE的视频流业务传输控制优化机制,该优化机制联合了丢包率与端到端单向时延增减趋势信息对网络状态进行细分并判断网络拥塞程度,视频流业务发送端根据监测的网络状态与由基于RBFN的QoE估计模型计算的用户体验质量,采取相应的策略动态地调整发送端的传输速率,即编码比特率,以达到提升用户体验质量的目的。实验结果表明,我们提出的基于RBFN的QoE估计模型的业务传输控制策略在网络状态波动的情况下,能够实现视频流业务QoE的提升,并且具有良好的视频播放稳定性。
[Abstract]:With the rapid development of wireless communication technology, wireless video streaming services are more and more widely used. In order to obtain users' recognition of video streaming service, service providers urgently need to establish a quality evaluation system based on the degree of user acceptance. Traditional quality of service (Quality of Service, QoS) is a widely used method of service measurement, but QoS emphasizes the objective evaluation index at the technical level, which can not directly reflect the users' true feeling about the quality of video stream. User experience quality (Quality of Experience, QoE) is a service measurement method based on the degree of user acceptance. It can evaluate the influence of various QOS indexes on the user experience. QoE directly reflects the user's approval of the service and is the key factor to determine the success of wireless video streaming service. Therefore, the quality of user experience is not only the focus of academic research, but also the focus of business development in industry. In order to guarantee the user experience quality of wireless network video stream service, it is of great significance and practical value to evaluate the quality of wireless video stream service and its application optimization. This paper focuses on the QoE, of wireless video streaming services; the user experience quality estimation model of video streaming services in wireless networks and its application. In the aspect of user experience quality estimation of wireless video stream service, the existing QoE estimation methods have some problems, such as the evaluation index is not comprehensive, the evaluation accuracy is not ideal, and so on. In this paper, a non-reference quality evaluation model based on radial basis function neural network (Radial Basis Function Neural Networks, RBFN) is proposed. Firstly, we analyze the effect of end-to-end cross layer parameters on the quality of video stream service user experience. Then, the QoE estimation model based on RBFN is established, and the evaluation principle and flow of QoE estimation model based on RBFN are described in detail. Finally, we simulate and verify the proposed QoE estimation model and compare it with the other four typical non-reference estimation models. The results show that the proposed QoE estimation model based on RBFN is not only the most accurate. And it has low time complexity. Aiming at the application of QoE estimation model in wireless video stream traffic optimization, this paper proposes an optimization mechanism of video stream traffic transmission control based on QoE. The optimization mechanism combines packet loss rate and end-to-end one-way delay trend information to subdivide the network state and judge the degree of network congestion. According to the monitored network status and the user experience quality calculated by the QoE estimation model based on RBFN, the video stream service sender dynamically adjusts the transmission rate of the sender, that is, the coded bit rate, by adopting the corresponding strategy. In order to improve the quality of user experience. The experimental results show that the proposed service transmission control strategy based on RBFN QoE estimation model can achieve the enhancement of video stream service QoE under the condition of network state fluctuation and has good video playback stability.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN919.8
本文编号：2282718
[Abstract]:With the rapid development of wireless communication technology, wireless video streaming services are more and more widely used. In order to obtain users' recognition of video streaming service, service providers urgently need to establish a quality evaluation system based on the degree of user acceptance. Traditional quality of service (Quality of Service, QoS) is a widely used method of service measurement, but QoS emphasizes the objective evaluation index at the technical level, which can not directly reflect the users' true feeling about the quality of video stream. User experience quality (Quality of Experience, QoE) is a service measurement method based on the degree of user acceptance. It can evaluate the influence of various QOS indexes on the user experience. QoE directly reflects the user's approval of the service and is the key factor to determine the success of wireless video streaming service. Therefore, the quality of user experience is not only the focus of academic research, but also the focus of business development in industry. In order to guarantee the user experience quality of wireless network video stream service, it is of great significance and practical value to evaluate the quality of wireless video stream service and its application optimization. This paper focuses on the QoE, of wireless video streaming services; the user experience quality estimation model of video streaming services in wireless networks and its application. In the aspect of user experience quality estimation of wireless video stream service, the existing QoE estimation methods have some problems, such as the evaluation index is not comprehensive, the evaluation accuracy is not ideal, and so on. In this paper, a non-reference quality evaluation model based on radial basis function neural network (Radial Basis Function Neural Networks, RBFN) is proposed. Firstly, we analyze the effect of end-to-end cross layer parameters on the quality of video stream service user experience. Then, the QoE estimation model based on RBFN is established, and the evaluation principle and flow of QoE estimation model based on RBFN are described in detail. Finally, we simulate and verify the proposed QoE estimation model and compare it with the other four typical non-reference estimation models. The results show that the proposed QoE estimation model based on RBFN is not only the most accurate. And it has low time complexity. Aiming at the application of QoE estimation model in wireless video stream traffic optimization, this paper proposes an optimization mechanism of video stream traffic transmission control based on QoE. The optimization mechanism combines packet loss rate and end-to-end one-way delay trend information to subdivide the network state and judge the degree of network congestion. According to the monitored network status and the user experience quality calculated by the QoE estimation model based on RBFN, the video stream service sender dynamically adjusts the transmission rate of the sender, that is, the coded bit rate, by adopting the corresponding strategy. In order to improve the quality of user experience. The experimental results show that the proposed service transmission control strategy based on RBFN QoE estimation model can achieve the enhancement of video stream service QoE under the condition of network state fluctuation and has good video playback stability.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN919.8
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，本文编号：2282718
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