Selected Publications

The contribution of this paper is the definition of a generic QoE fairness index $F$ which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain’s fairness index. Based on results, we argue that neither QoS fairness nor Jain’s fairness index meet all of the desirable QoE-relevant properties which are met by $F$.
IEEE Communications Letters, vol. 21, no. 1, pp. 184-187

While Quality of Experience (QoE) has advanced very significantly as a field in recent years, the methods used for analyzing it have not always kept pace. When QoE is studied, measured or estimated, practically all the literature deals with the so-called Mean Opinion Score (MOS). In this article we put forward the limitations of MOS, present other statistical tools that provide a much more comprehensive view of how quality is perceived by the users, and illustrate it all by analyzing the results of several subjective studies with these tool
Quality and User Experience 1(1):1-23, Springer.

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.
In proceedings of IEEE ICC QoE-FI, London, UK.

In this paper we propose a methodological framework for modeling Quality of Experience (QoE) for media services in a generic manner. We consider QoE as a multi-dimensional concept dependent on several factors related to the service itself, its resource requirements, its users, and its context of use.
In PIK - Praxis der Informationverarbeitung und -kommunikation, 37(4):265-274

In this paper we present a generic ARCU (Application-Resource-Context-User) Model which categorizes influence factors into four multi-dimensional spaces. The model further maps points from these spaces to a multi-dimensional QoE space, representing both qualitative and quantitative QoE metrics.
In proceedings of MIPRO 2012, Opatija, Croatia.

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.
IEEE Communications Magazine, 50(4):28-36

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. 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.
Computer Networks 52(6):1179-1192.

We propose a method for building real-time models for speech streams. Our method is based on using G-networks (open networks of queues with positive and negative customers) as Neural Networks (in this case, they are called Random Neural Networks) to learn, in some sense, how humans react vis-à-vis a speech signal that has been distorted by encoding and transmission impairments. We further apply our technique to study the impact on performance of several basic source and network parameters on a non-interactive speech flow, namely loss rate, loss distribution, codec, forward error correction, and packetization interval, all at the same time.
Performance Evaluation, 57(2):141-162

Recent Publications

More Publications

  • No Silver Bullet: QoE Metrics, QoE Fairness, and User Diversity in the Context of QoE Management

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  • Embracing Uncertainty: A Probabilistic View of HTTP Video Quality

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  • Definition of QoE Fairness in Shared Systems

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  • QoE beyond the MOS: an in-depth look at QoE via better metrics and their relation to MOS

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  • On Additive and Multiplicative QoS-QoE Models for Multiple QoS Parameters

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  • Generating Realistic YouTube-like Stall Patterns for HTTP Video Streaming Assessment

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  • Network Quality Differentiation: Regional Effects, Market Entrance, and Empirical Testability

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  • QoE - Defining a User-centric Concept for Service Quality

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  • A Layered Model for Quality Estimation of HTTP Video from QoS Measurements

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  • On the Analysis of QoE in Cellular Networks: from Subjective Tests to Large-scale Traffic Measurements

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Recent Posts

Introduction A few days ago I ran across this discussion on Hacker News, which referred to this article on binary data visualization. The folks at Codisec have developed a tool called Veles for visualizing binary files. The idea is to help detect patterns in the data, which in some cases are useful for e.g,. security-related analysis. The technique described was surprisingly simple, in that it looks at digrams or trigrams in the file, and then it analyses their frequency and spatial distribution within the data.

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