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the network. [42] N/A - It gives a comprehensive survey of the evolution of video quality assessment methods, analysing their characteristics, advantages, and drawbacks. - It also introduces QoE-based video applications and, identifies the future research directions of QoE- oriented video quality assessment. [114][115] Internet Service Provider (ISP) network, at Polytechnic University of Valencia, Spain. video QoE calculation based on several parameters: jitter, delay, bandwidth, loss packets and zapping time for IPTV video transmissions - They provide an automatic Video Quality Assessment (VQA) based on the identification and processing of parameters extracted from the video. -They present a QoE management system to guarantee enough IPTV QoE to the customer independently of its type of connection (wired or wireless). The system calculates the user’s QoE and notifies which networks are available and have higher QoE. -QoE estimates are based in a mathematical expression connecting several network measurements. It is not based in a machine learning algorithm. Emulation of - This works proposes an analytical expression for [116] N/A - They produce a theoretical discussion on how to use QoS parameters (e.g. delay, jitter...) to predict QoE using a dataset built from subjective end-user scores, and applying machine learning algorithms based on Support Vector Machine (SVM) and Decision Trees. [117] N/A - This work provides a survey of machine learning techniques used to capture the relationship between QoS parameters and QoE scores. - They apply most of the common machine learning algorithms (Linear Discriminant Analysis, Random Forest (RF), SVM, Naïve Bayes, K-Nearest Neighbors) to the automatic identification of QoE from QoS network parameters. [118] Dataset based on 40 million video viewing sessions on conviva.com’s affiliate content providers’ websites. - It focuses on Content Delivery Networks (CDN). - It gives a review of the reasons why developing an objective method of quality assessment based on video transmission parameters is extremely difficult due to the complex relationships between these parameters, the user’s perception and even the nature of the content. - They apply machine learning algorithms (Decision Trees, Naïve Bayes and Logistic Regression) to predict the QoE based on transmission parameters (bitrates, latency...) and end-user engagement attributes (playtime, number of visits...). [119] LIVE-Netflix Video QoE Database - They perform prediction of streaming video QoE applying several regression models such as Ridge and Lasso Regression, and ensemble methods such as Random Forest (RF), Gradient Boosting (GB) and Extra Trees (ET). Table 4. QoE estimation - related works Doctoral Thesis: Novel applications of Machine Learning to NTAP - 36PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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