The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.

Next Generation 5G Wireless Networks: A Comprehensive Survey

The Transmission Control Protocol.

(1) Whereas the objectives of the Community, as laid down in the Treaty, as amended by the Treaty on European Union, include creating an ever closer union among the peoples of Europe, fostering closer relations between the States belonging to the Community, ensuring economic and social progress by common action to eliminate the barriers which divide Europe, encouraging the constant improvement of the living conditions of its peoples, preserving and strengthening peace and liberty and promoting democracy on the basis of the fundamental rights recognized in the constitution and laws of the Member States and in the European Convention for the Protection of Human Rights and Fundamental Freedoms;

Some Preliminary Comments on the DIRECTIVE 95/46/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data.

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

A Survey on Quality of Experience of HTTP Adaptive Streaming

This White Paper is a contribution of the European Network on Quality of Experience in Multimedia Systems and Services, Qualinet (COST Action IC 1003, see www.qualinet.eu), to the scientific discussion about the term "Quality of Experience" (QoE) and its underlying concepts. It resulted from the need to agree on a working definition for this term which facilitates the communication of ideas within a multidisciplinary group, where a joint interest around multimedia communication systems exists, however approached from different perspectives. Thus, the concepts and ideas cited in this paper mainly refer to the Quality of Experience of multimedia communication systems, but may be helpful also for other areas where QoE is an issue. The Network of Excellence (NoE) Qualinet aims at extending the notion of network-centric Quality of Service (QoS) in multimedia systems, by relying on the concept of Quality of Experience (QoE). The main scientific objective is the development of methodologies for subjective and objective quality metrics taking into account current and new trends in multimedia communication systems as witnessed by the appearance of new types of content and interactions. A substantial scientific impact on fragmented efforts carried out in this field will be achieved by coordinating the research of European experts under the catalytic COST umbrella. The White Paper has been compiled on the basis of a first open call for ideas which was launched for the February 2012 Qualinet Meeting held in Prague, Czech Republic. The ideas were presented as short statements during that meeting, reflecting the ideas of the persons listed under the headline "Contributors" in the previous section. During the Prague meeting, the ideas have been further discussed and consolidated in the form of a general structure of the present document. An open call for authors was issued at that meeting, to which the persons listed as "Authors" in the previous section have announced their willingness to contribute in the preparation of individual sections. For each section, a coordinating author has been assigned which coordinated the writing of that section, and which is underlined in the author list preceding each section. The individual sections were then integrated and aligned by an editing group (listed as "Editors" in the previous section), and the entire document was iterated with the entire group of authors. Furthermore, the draft text was discussed with the participants of the Dagstuhl Seminar 12181 "Quality of Experience: From User Perception to Instrumental Metrics" which was held in Schlos Dagstuhl, Germany, May 1-4 2012, and a number of changes were proposed, resulting in the present document. As a result of the writing process and the large number of contributors, authors and editors, the document will not reflect the opinion of each individual person at all points. Still, we hope that it is found to be useful for everybody working in the field of Quality of Experience of multimedia communication systems, and most probably also beyond that field.

https://hal.archives-ouvertes.fr/hal-00977812/document

Qualinet White Paper on Definitions of Quality of Experience

This paper addresses the challenge of assessing and modeling Quality of Experience (QoE) for online video services that are based on TCP-streaming. We present a dedicated QoE model for You Tube that takes into account the key influence factors (such as stalling events caused by network bottlenecks) that shape quality perception of this service. As second contribution, we propose a generic subjective QoE assessment methodology for multimedia applications (like online video) that is based on crowd sourcing - a highly cost-efficient, fast and flexible way of conducting user experiments. We demonstrate how our approach successfully leverages the inherent strengths of crowd sourcing while addressing critical aspects such as the reliability of the experimental data obtained. Our results suggest that, crowd sourcing is a highly effective QoE assessment method not only for online video, but also for a wide range of other current and future Internet applications.

Quantification of YouTube QoE via Crowdsourcing

HTTP Adaptive Streaming (HAS) is employed by more and more video streaming services in the Internet. It allows to adapt the downloaded video quality to the current network conditions, and thus, avoids stalling (i.e., playback interruptions) to the greatest possible extend. The adaptation of video streams is done by switching between different quality representation levels, which influences the user perceived quality of the video stream. In this work, the influence of several adaptation parameters, namely, switch amplitude (i.e., quality level difference), switching frequency, and recency effects, on Quality of Experience (QoE) is investigated. Therefore, crowdsourcing experiments were conducted in order to collect subjective ratings for different adaptation-related test conditions. The results of these subjective studies indicate the influence of the adaptation parameters, and based on these findings a simplified QoE model for HAS is presented, which only relies on the switch amplitude and the playback time of each layer.

Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming

The performance of YouTube in mobile networks is crucial to network operators, who try to find a trade-off between cost-efficient handling of the huge traffic amounts and high perceived end-user Quality of Experience (QoE). This paper introduces YoMoApp (YouTube Performance Monitoring Application), an Android application, which passively monitors key performance indicators (KPIs) of YouTube adaptive video streaming on end-user smartphones. The monitored KPIs (i.e., player state/events, buffer, and video quality level) can be used to analyze the QoE of mobile YouTube video sessions. YoMoApp is a valuable tool to assess the performance of mobile networks with respect to YouTube traffic, as well as to develop optimizations and QoE models for mobile HTTP adaptive streaming. We test YoMoApp through real subjective QoE tests showing that the tool is accurate to capture the experience of end-users watching YouTube on smartphones.

YoMoApp: A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks

YouTube is changing the way operators manage network performance monitoring. In this paper we introduce YOUQMON, a novel on-line monitoring system for assessing the Quality of Experience (QoE) undergone by HSPA/3G customers watching YouTube videos, using network-layer measurements only. YOUQMON combines passive traffic analysis techniques to detect stalling events in YouTube video streams, with a QoE model to map stallings into a Mean Opinion Score reflecting the end-user experience. We evaluate the stalling detection performance of YOUQMON with hundreds of YouTube video streams, and present results showing the feasibility of performing real-time YouTube QoE monitoring in an operational mobile broadband network.

Michael Seufert

Papers

A Survey on Quality of Experience of HTTP Adaptive Streaming

Quantification of YouTube QoE via Crowdsourcing

Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming

YoMoApp: A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks

YOUQMON: a system for on-line monitoring of YouTube QoE in operational 3G networks