Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones

International audienceMeasuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and user-behavior-related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations

On the analysis of youTube QoE in cellular networks through in-smartphone measurements

International audienceCellular-network operators are becoming increasingly interested in knowing the Quality of Experience (QoE) of their customers. QoE measurements represent today a main source of information to monitor, analyze, and subsequently manage operational networks. In this paper, we focus on the analysis of YouTube QoE in cellular networks, using QoE and distributed network measurements collected in real users' smart-phones. Relying on YoMoApp, a well-known tool for collecting YouTube smartphone measurements and QoE feedback in a crowdsourcing fashion, we have built a dataset covering about 360 different cellular users around the globe, throughout the past five years. Using this dataset, we study the characteristics of different QoE-relevant features for YouTube in smartphones. Measurements reveal a constant improvement of YouTube QoE in cellular networks over time, as well as an enhancement of the YouTube video streaming functioning in smartphones. Using the gathered measurements, we additionally investigate two case studies for YouTube QoE monitoring and analysis in cellular networks: the machine-learning-based prediction of QoE-relevant metrics from network-level measurements, and the modeling and assessment of YouTube QoE and user engagement in cellular networks and smartphone devices. Last but not least, we introduce the YoMoApp cloud dashboard to openly share smartphone YouTube QoE measurements, which allows anyone using the YoMoApp smartphone app to get immediate access to all the raw measurements collected at her devices

Mobile web and app QoE monitoring for ISPs - from encrypted traffic to speed index through machine learning

International audienceWeb browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

A Model for Mapping Combined Effects of Quality of Service Parameters and Device Features on Video Streaming Quality of Experience

Maintaining quality of streaming video is challenged by network faults resulting into freezes and rebufferings on the video. On top of the network effects, device features have impacts on the image of the video frames displayed during streaming. Despite the simultaneous impacts of video quality from network and device, previous studies considered individual impact of network parameters or devices as influencing factors to propose Quality of Experience (QoE) models. This study proposed QoE model by mapping combined effects from both network and device on video streamed QoE. An experiment to study the effects of video quality from combined effects of network and device over the wireless involved 35 subjects. Combination of packet loss, packet reordering, and delay were emulated using network emulator following Design of Experiment methodology. Through analysis of variance, the study found that packet loss had the highest impact, followed by device features, reordering, and delay on the video QoE. From the combined effects, two-way interactions and three-way interactions had significant effects on the video QoE. Through additive and linearity behavior of the input factors from network and device on video streaming QoE, a multi-factor model was derived. Keywords: Design of Experiment (DOE); Mean Opinion Score (MOS); Quality of Experience (QoE); Quality of Service (QoS); Video Quality Assessmen