無線視頻流業(yè)務的用戶體驗質(zhì)量估計模型及其應用
發(fā)布時間：2018-10-20 09:24
【摘要】：隨著無線通信技術(shù)的高速發(fā)展,無線視頻流業(yè)務應用越來越廣泛,人們對無線視頻流的服務質(zhì)量期望也逐漸提高。為了獲得用戶對視頻流服務的認可,服務提供商迫切需要建立一種以用戶認可程度為標準的質(zhì)量評價體系。傳統(tǒng)的服務質(zhì)量(Quality of Service, QoS)是一種被廣泛采用的服務度量方法,但是QoS強調(diào)技術(shù)層面的客觀評價指標,不能直接體現(xiàn)用戶對視頻流質(zhì)量的真實感受。用戶體驗質(zhì)量(Quality of Experience, QoE)是一種以用戶認可程度為標準的服務度量方法,它以用戶對業(yè)務的使用感受為研究重點,能評價業(yè)務中多種QOS指標對用戶體驗的影響。QoE直接反映了用戶對服務的認可程度,是決定無線視頻流業(yè)務能否取得成功的關(guān)鍵因素。因此,用戶體驗質(zhì)量不僅是學術(shù)界的重點研究課題,也是工業(yè)界實現(xiàn)業(yè)務發(fā)展關(guān)注的焦點。為了保證無線網(wǎng)絡視頻流業(yè)務的用戶體驗質(zhì)量,對無線視頻流業(yè)務進行質(zhì)量評估及其應用優(yōu)化有重要的研究意義和實用價值。本文圍繞無線視頻流業(yè)務的QoE,研究無線網(wǎng)絡中視頻流業(yè)務的用戶體驗質(zhì)量估計模型及其應用。 在無線視頻流業(yè)務用戶體驗質(zhì)量估計方面,針對現(xiàn)有QoE估計方法存在的評估指標不全面、評估準確度不理想等問題,本文提出了一種基于徑向基函數(shù)神經(jīng)網(wǎng)絡(Radial Basis Function Neural Networks, RBFN)的無參考質(zhì)量評估模型。首先,我們分析了端到端跨層參數(shù)對視頻流業(yè)務用戶體驗質(zhì)量產(chǎn)生的影響。然后,在無需原視頻作比較的前提下,建立了基于RBFN的QoE估計模型,詳細闡述了基于RBFN的QoE估計模型的評估原理與流程。最后,我們仿真驗證所提出的QoE估計模型,并與其它四種典型的無參考估計模型進行比較分析,結(jié)果表明我們所提出的基于RBFN的QoE估計模型不僅評估準確度最高,而且具有低的時間復雜度。 針對QoE估計模型在無線視頻流業(yè)務優(yōu)化的應用方面,本文提出了一種基于QoE的視頻流業(yè)務傳輸控制優(yōu)化機制,該優(yōu)化機制聯(lián)合了丟包率與端到端單向時延增減趨勢信息對網(wǎng)絡狀態(tài)進行細分并判斷網(wǎng)絡擁塞程度,視頻流業(yè)務發(fā)送端根據(jù)監(jiān)測的網(wǎng)絡狀態(tài)與由基于RBFN的QoE估計模型計算的用戶體驗質(zhì)量,采取相應的策略動態(tài)地調(diào)整發(fā)送端的傳輸速率,即編碼比特率,以達到提升用戶體驗質(zhì)量的目的。實驗結(jié)果表明,我們提出的基于RBFN的QoE估計模型的業(yè)務傳輸控制策略在網(wǎng)絡狀態(tài)波動的情況下,能夠?qū)崿F(xiàn)視頻流業(yè)務QoE的提升,并且具有良好的視頻播放穩(wěn)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.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN919.8
本文編號：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.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN919.8
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相關(guān)期刊論文 前2條
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2 邱錦波;朱光喜;;一種無線視頻傳輸?shù)目鐚幼赃m應不平等保護方法[J];電子與信息學報;2007年01期
，本文編號：2282718
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