Latency Target based Analysis of the DASH.js Player

We analyse the low latency performance of the three Adaptive Bitrate (ABR) algorithms in the dash.js Dynamic Adaptive Streaming over HTTP (DASH) player with respect to a range of latency targets and configuration options. We perform experiments on our DASH Testbed which allows for testing with a range of real world derived network profiles. Our experiments enable a better understanding of how latency targets affect quality of experience (QoE), and how well the different algorithms adhere to their targets. We find that with dash.js v4.5.0 the default Dynamic algorithm achieves the best overall QoE. We show that whilst the other algorithms can achieve higher video quality at lower latencies, they do so only at the expense of increased stalling. We analyse the poor performance of L2A-LL in our tests and develop modifications which demonstrate significant improvements. We also highlight how some low latency configuration settings can be detrimental to performance.Comment: To be published in Proceedings of the 14th ACM Multimedia Systems Conference (MMSys '23), June 7-10, 2023, Vancouver, BC, Canad

Privacy issues of ISPs in the modern web

In recent years, privacy issues in the networking field are getting more important. In particular, there is a lively debate about how Internet Service Providers (ISPs) should collect and treat data coming from passive network measurements. This kind of information, such as flow records or HTTP logs, carries considerable knowledge from several points of view: traffic engineering, academic research, and web marketing can take advantage from passive network measurements on ISP customers. Nevertheless, in many cases collected measurements contain personal and confidential information about customers exposed to monitoring, thus raising several ethical issues. Modern web is very different from the one we experienced few years ago: web services converged to few protocols (i.e., HTTP and HTTPS) and a large share of traffic is encrypted. The aim of this work is to provide an insight about which information is still visible to ISPs, with particular attention to novel and emerging protocols, and to what extent it carries personal information. We illustrate that sensible information, such as website history, is still exposed to passive monitoring. We illustrate privacy and ethical issues deriving by the current situation and provide general guidelines and best practices to cope with the collection of network traffic measurements

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

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

Beauty is in the Eye of the Smartphone Holder - A Data Driven Analysis of YouTube Mobile QoE

International audienceMeasuring the Quality of Experience (QoE) undergone by cellular network users has become paramount for cellular ISPs. Given its overwhelming dominance and ever-growing popularity, this paper focuses on the analysis of QoE for YouTube in mobile networks. Using a large-scale dataset of crowdsourced YouTube QoE measurements collected in smartphones with YoMoApp, we analyze the evolution of multiple relevant QoE-related metrics over time for YouTube mobile users. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our data-driven analysis shows a systematic performance and QoE improvement of YouTube in mobile devices over time, accompanied by an improvement of cellular network performance and by an optimization of the YouTube streaming behavior for smartphones

An Online Sampling Approach for Controlled Experimentation and QoE Modeling

International audiencePredicting Quality of Experience (QoE) of end users from available network Quality of Service (QoS) measurements is of significant importance for today’s network and content providers. This can be achieved by using application-specific QoE models that map the network QoS to the output QoE. QoS-QoE models can be built by training supervised machine learning algorithms with training data consisting of the mappings of theinput network QoS to the output QoE. In most ML works on QoE modeling, the training data is usually gathered in the wild inside the core of the service or the content provider networks. However, such data is not easily accessible to the general research community. Consequently, the training data if not available before hand, needs to be built up by controlled experimentation. Here, the fundamental challenge is the sheer amount oftime consumed in collecting the datasets needed to model the QoE. Considering this problem, we present here a framework of controlled experimentation based on active learning, that allows collecting rich datasets covering the experimental space intelligently. We perform a rigorous analysis of our approach and demonstrate the performance improvement over conventional pool based uncertainty sampling for a particular use case of YouTube video streaming