Machine Learning approach for Quality of Experience aware networks

Proceedings of the 7th …, 2009

2013

This paper deals with the video quality evaluation of mobile video delivery. It analyzes the current trends in video quality evaluation while trying to shift video quality measurement from service-centered to more user-centered. The attention is in defining the causalities among specific video and network parameters, and perceived video quality. As a part of this target, vast subjective video quality survey has been conducted to define these causalities. These causalities were particularly important in designing of our video quality prediction model based on Bayesian network. Proposed solution and prediction results were then experimentally evaluated compared to video quality results of independent subjective video quality assessment.

… and Communications (ISCC) …, 2012

Quality of Experience (QoE) provides human centric assessment of multimedia quality. QoE of a multimedia service is affected by various application and network layer QoS parameters; content and business parameters. In this paper, we propose a new QoE framework for Multimedia services (named as QoM) for run time quality evaluation of video streaming services based on the influence of QoE factors, various network and application level QoS parameters. The newly proposed QoM framework monitors QoS and QoE data, evaluates it and moreover, in the event of decline in QoE, it can also send alert messages to the Administrator based on some policy rules. Our new QoM framework is being launched as an open-source QoE evaluation tool for the industry and research community. More realistic experimental work is also underway and we intend to conduct user tests to evaluate the performance of the proposed QoM framework in a context of real 4G WiMax wireless networks.

ACM SIGMETRICS Performance Evaluation Review, 2008

This article describes a full video delivery network monitoring suite. Our monitoring tool offers a new view of a video delivery network, based on the quality as perceived by final users (what is nowadays called Quality of Experience, in short QoE). We measure the perceived quality at the client side by means of the recently proposed PSQA technology, by studying the video flows at the frame level. The developed monitoring suite is a completely free-software application, based on well-known technologies such as Simple Network Management Protocol or Round Robin Databases, which can be executed in various operating systems. In this short article we explain the tool implementation and we present some of the measurements performed with it.

Quality-of-Experience (QoE) is a human centric notion that produces the blue print of human perception, feelings, needs and intentions while Quality-of-Service (QoS) is a technology centric metric used to assess the performance of a multimedia application and/or network. To ensure superior video QoE, it is important to understand the relationship between QoE and QoS. To achieve this goal, we conducted a pilot subjective user study simulating a video streaming service over a broadband network with varying distortion scenarios, namely packet losses (0, 0.5, 1, 3,7, and 15%), packet reorder (0, 1, 5, 10, 20, and 30%), and coding bit rates (100, 400, 600, and 800 Kbps). Users were asked to rate their experience using a subjective quantitative metric (termed Perceived Video Quality, PVQ) and qualitative indicators of “experience.” Simulation results suggest a) an exponential relationship between PVQ and packet loss and between PVQ and packet reorder, and b) a logarithmic relationship between PVQ and video bit rate. Similar trends were observed with the qualitative indicators. Exploratory analysis with two objective video quality metrics suggests that trends similar to those obtained with the subjective ratings were obtained, particularly with a full-reference metric.

annals of telecommunications - annales des télécommunications, 2009

The impact of network performance on user experience is important to know, as it determines the success or failure of a service. Unfortunately, it is very difficult to assess it in real-time on an operational network. Monitoring of network-level performance criteria is easier and more usual. But the problem is then to correlate these network-level Quality of Service (QoS) to the Quality of Experience (QoE) perceived by the users. Efforts have been done in the previous years to map user behaviour to traffic characteristics on the network to QoS. However, being able to successfully relate these traffic characteristics to user satisfaction is not a simple task and still requires further investigations. In this work, we try to associate on one side the correlations between various traffic characteristics measured on an operational network and on the other side the user experience tested on an experimental platform. Our aim is to observe some pronounced trends regarding relationships between both types of results. More precisely, we want to validate how and to what extent the volumes of user sessions represent the level of user satisfaction. Along this way, we need to revise classical relationships between some of the network performance indicators such as loss, download time and throughput in order to strengthen the understanding of this impact on each other and on user satisfaction. This preliminary study is based on the application web.

From Evaluation to Monitoring, 2014

Electronics, 2021

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...

2010

In the last decade, networks have evolved from simple data packet forwarding to platforms that support complex multimedia services, such as network-based personal video recording and broadcast TV. Each of these services has significant quality demands: they are very sensitive to packet loss and jitter, and require a substantial amount of bandwidth. As the quality perceived by the end user gives the most accurate view on the streamed service quality, operators are increasing their focus on this type of metric, commonly described as Quality of Experience. This paper presents the design of a Quality of Experience information model that defines important metrics for measuring service quality. Based on these metrics, we define a novel control loop that represents the relationships among Quality of Experience, the Customer, and network services.

2010

The increase in multimedia content on the Internet has created a renewed interest in quality assessment. There is however a main difference from the traditional quality assessment approaches, as now, the focus relies on the user perceived quality, opposed to the network centered approach classically proposed. In this paper we overview the most relevant challenges to perform Quality of Experience (QoE) assessment in IP networks and highlight the particular considerations necessary when compared to alternative mechanisms, already deployed, such as Quality of Service (QoS). To assist on the handling of such challenges we first discuss the different approaches to Quality of Experience assessment along with the most relevant QoE metrics, and then we discuss how they are used to provide objective results about user satisfaction.