Martín Varela

Challenges of QoE management for cloud applications

Cloud computing is currently gaining enormous momentum due to a number of promised benefits: ease of use in terms of deployment, administration, and maintenance, along with high scalability and flexibility to create new services. However, as more personal and business applications migrate to the cloud, service quality will become an important differentiator between providers. In particular, quality of experience as perceived by users has the potential to become the guiding paradigm for managing quality in the cloud. In this article, we discuss technical challenges emerging from shifting services to the cloud, as well as how this shift impacts QoE and QoE management. Thereby, a particular focus is on multimedia cloud applications. Together with a novel QoE-based classification scheme of cloud applications, these challenges drive the research agenda on QoE management for cloud applications.

Quality assessment of interactive voice applications

The conversational quality of a VoIP communication is dependent on several factors such as the coding process used, the network conditions and the type of error correction or concealment employed. Furthermore, the quality perceived by the users is also dependent on the characteristics of the conversation itself. Assessing this kind of communication is a very difficult problem, and most of the studies available in the literature simplify the issue by restricting the analysis to only one or two parameters. However, the number of potentially affecting factors is typically higher, and their joint effect on quality is complex. In this paper we study the combined effects of bit rate, forward error correction, loss rate, loss distribution, delay and jitter on the perceived conversational quality. In order to achieve this we use the pseudo-subjective quality assessment (PSQA) technique, which allows us to obtain accurate, subjective-like assessments, in real time if necessary. Our contributions are thus twofold: firstly, we offer a detailed analysis of the impact of these parameters and their interactions on the perceived conversational quality. Secondly, we show how the PSQA methodology can be used to provide accurate conversational quality estimations.

Evaluating users’ satisfaction in packet networks using random neural networks

Quantifying the quality of a video or audio transmission over the Internet is usually a hard task, as based on the statistical processing of the evaluations made by a panel of humans (the corresponding and standardized area is called subjective testing). In this paper we describe a methodology called Pseudo-Subjective Quality Assessment (PSQA), based on Random Neural Networks, which is able to perform this task automatically, accurately and efficiently. RNN had been chosen here because of their good performances over other possibilities; this is discussed in the paper. Some new insights on PSQA’s use and performance are also given. In particular we discuss new results concerning PSQA–based dynamic quality control, and conversational quality assessment.

QOE-driven mobility management — Integrating the users' quality perception into network — Level decision making

One of the most interesting applications of single — sided Quality of Experience (QoE) metrics is their use in improving the quality of the service, as perceived by the user. This can be done either at the application level — by for example changing the encoding in use, or the level of error correction applied — or at the network level, for instance by choosing a different DiffServ marking strategy, or changing the access network in use. To this end, the QoE metric used needs to be fast and accurate, and the context in which the application will be used needs to provide the opportunity for performing some sort of control operation. In this paper we describe the application of QoE estimations for VoIP to improve existing network — level mobility management solutions.

From Service Level Agreements (SLA) to Experience Level Agreements (ELA): The challenges of selling QoE to the user

In contrast to the rather network-centric notion of Quality of Service (QoS), the concept of Quality of Experience (QoE) has a strongly user-centric perspective on service quality in communication networks as well as online services. However, related research on QoE so far has largely neglected the question of how to operationalize quality differentiation and to provide corresponding solutions tailored to the end users. In this paper, we argue that the introduction of Experience Level Agreements (ELA) as QoE-enabled counterpiece to traditional QoS-based Service Level Agreements (SLA) would provide a key step towards being able to sell service quality to the user. Hence, we investigate various ideas to exploit QoE awareness for improving SLAs (ranging from internal aspects like SLOs by service providers to completely novel definitions of ELAs which are able to characterize QoE explicitly), and discuss important problems and challenges of the proposed transition as well.

QoE-driven network management for real-time over-the-top multimedia services

This paper introduces a network access point (AP) control solution in the context of over-the-top (OTT) multimedia services. The solution is designed to provide network-level management mechanisms for packet traffic while using Quality of Experience (QoE) as a performance indicator. The results showed that with customer subscription scheme, traffic differentiation and QoE-driven management it is possible to both improve the perceived quality of multimedia traffic and increase the average revenue per user.

Evaluating the Utility of Media–Dependent FEC in VoIP Flows

In this paper, we present an analysis of the impact of using media-dependent Forward Error Correction (FEC) in VoIP flows over the Internet. This error correction mechanism consists of piggy-backing a compressed copy of the contents of packet n in packet n + i (i being variable), so as to mitigate the effect of network losses on the quality of the conversation. To evaluate the impact of this technique on the perceived quality, we propose a simple network model, and study different scenarios to see how the increase in load produced by FEC affects the network state. We then use a pseudo-subjective quality evaluation tool that we have recently developed in order to assess the effects of FEC and the affected network conditions on the quality as perceived by the end-user.

A reduced-reference parametric model for audiovisual quality of IPTV services

In this paper we present a parametric model for audiovisual quality estimation in IPTV and similar services. The proposed model takes advantage of signal characteristics calculated at the sender (in particular related to levels of motion in the content), but is purely parametric on the estimation (i.e. it does not require peeking into the bitstream), which makes it suitable for large-scale real-time monitoring applications. In order to obtain the model, we followed the Pseudo-Subjective Quality Assessment (PSQA) methodology, and compared different kinds of statistical estimators, namely Multilayer Perceptrons (MLP) and Random Neural Networks (RNN).

Quality of Service Versus Quality of Experience

It is often the case that in the current literature, the term “QoE” is used in contexts where “QoS” would be more appropriate. This is likely due to several reasons, one of which being the current popularity of all things related with QoE, but more fundamentally it is due to the boundaries between QoS and QoE not being clearly defined—and indeed, sometimes hard to define clearly. QoE is an intrinsically multi-disciplinary field, and practitioners from different backgrounds see it, quite naturally, from different perspectives colored by their own expertise. For networking people, in particular, QoE is sometimes seen as a simple extension, or even a re-branding, of the well-established concept of QoS. In this chapter we will delve into the differences and commonalities between the two, with the goal of easing and encouraging their proper use.

Towards an understanding of visual appeal in website design

Visual appeal (VA), as judged by users accessing different websites, has been shown to impact overall user preferences and satisfaction. In the context of Web Quality of Experience (QoE), we hypothesize that VA has a non-trivial impact on end user quality perception and hence needs to be accounted for when modeling overall Web QoE. In this paper, we aim to quantify the impact of VA factors on perceived visual design quality for different types of tested websites. We carried out two large-scale (>350 users each) crowd-sourced campaigns to test the influence of several factors often found in design best practices recommendations. While results have identified color goodness and font goodness as Key Influence Factors (KIF) for VA, an analysis of user demographics has further shown a strong impact of users origin on subjective ratings. By quantifying the impact of identified KIFs on VA, we provide guidelines for successful VA manipulations to be considered in future Web QoE subjective tests.