Quality Of Experience Domain Research Articles

QoE-driven Antenna Tuning in Cellular Networks With Cooperative Multi-agent Reinforcement Learning

Antenna tuning plays an essential role in ensuring high quality wireless communications. Targeting for higher Quality of Service (QoS), many existing network antenna tuning schemes are based on expert knowledge, rule-based policies or conventional optimization theory. However, maximizing the traffic-related QoS does not guarantee that all customers experience good services. In addition, existing schemes are often limited to some handcrafted rules or heuristics and lack of adaptability especially in a time-varying environment. Quality of Experience (QoE), a user-centric metric, can better measure users' satisfaction for services in wireless networks. This paper proposes the cooperative tuning of antennas based on QoE, a paradigm shift from network-centric QoS to user-centric QoE domain. In a normal cellular network, besides the need of improving the overall QoE, it requires handling faults from different cells. As Multi-agent Reinforcement Learning (MARL) has the capability of self-learning the dynamics of environment, we propose an antenna configuration algorithm based on multi-goal MARL. In our framework, agents from different cells not only need to cooperate with each other to achieve the global goal of increasing the overall QoE of the wireless network but also complete some personal goals by combating the faults encountered in their own cells. To accelerate the training efficiency, we introduce a novel two-stage curriculum learning. To reduce the collection time of each QoE sample, we develop an accurate and timely QoE/QoS mapping model with the cascading of a Random Forest Classifier (RFC) and a Deep Neural Network (DNN) (abbreviated as RFC-DNN), which can help us obtain QoE by collecting QoS measurements and perform QoE-based antenna configurations with smaller time granularity. Our proposed RFC-DNN model can reduce the time by 70% when predicting the QoE of a single sample. A huge amount of time will be saved in MARL when tens of thousands of transitions/samples need to be collected. The performance results show that our proposed antenna tuning schemes can not only address specific faults in each cell, but also significantly improve the global average QoE with a faster and more stable convergence speed.

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At the same time, research is also going on trying to map the two different domains of quality metrics, i.e., the QoS and QoE domain. Apart from the work done by individual researchers, the International Telecommunications Union (ITU) has been quite active in this area of quality assessment. This is obvious from the large number of ITU standards that are available for different application types. The sheer variety of techniques being employed by ITU as well as other researchers sometimes tends to be too complex and diversified. Although there are survey papers that try to present the current state of the art methodologies for video quality evaluation, none has focused on the ITU perspective. In this work, we try to fill up this void by presenting up-to-date information on the different measurement methods that are currently being employed by ITU for a video streaming scenario. We highlight the outline of each method with sufficient detail and try to analyze the challenges being faced along with the direction of future research.

A new QoE fairness index for QoE management

The user-centric management of networks and services focuses on the Quality of Experience (QoE) as perceived by the end user. In general, the goal is to maximize (or at least ensure an acceptable) QoE, while ensuring fairness among users, e.g., in terms of resource allocation and scheduling in shared systems. A problem arising in this context is that the notions of fairness commonly applied in the QoS domain do not translate well to the QoE domain. We have recently proposed a QoE fairness index F, which solves these issues. In this paper, we provide a detailed rationale for it, along with a thorough comparison of the proposed index and its properties against the most widely used QoS fairness indices, showing its advantages. We furthermore explore the potential uses of the index, in the context of QoE management and describe future research lines on this topic.

Survey of research on Quality of Experience modelling for web browsing

A significant amount of research has been published to date studying various measures and influence factors related to the user experience when browsing Web content on different devices. For the most part these studies come from two different communities: the Quality of Experience (QoE) community and the User Experience (UX) community, and span different disciplines. While the QoE community has primarily focused on technical aspects and subjective perception of waiting times, the UX community has been working on issues of acceptance, experience, and crucial design factors extensively for a long time. This paper aims to provide a survey of literature related to QoE modelling for Web browsing by addressing studies that deal with the impact of a wide set of system, context, and human influence factors. The survey shows that the QoE community has for the most part neglected relevant aspects studied by the UX community, which are needed for a more holistic understanding of Web QoE. On the other hand, UX studies may benefit from insights into research conducted in the QoE domain in terms of the impact of more technical factors on UX. Thus, by bridging these findings we argue the need for future multidisciplinary and multidimensional studies on Web QoE modelling, whose product, that is, multidimensional Web QoE models, are of interest to multiple stakeholders involved in the service delivery chain. Readers of this paper will benefit from a systematic analysis of surveyed papers, summary of key findings, and a discussion of open research topics that contribute to setting a research agenda in this domain.