In recent years, the heterogeneity and variability of Internet applications, both in multimedia and in interactive environments, have increased substantially. Nowadays, video streaming is responsible for a large portion of traffic in the Internet. Internet telephony and video conferencing systems have become part of our daily life. At the same time, the Internet of Things strives to surpass any previous expectations towards the number of devices. Smartphones and tablets contribute to the ever‐growing demands on the networks. Furthermore, the proliferation of all types of media hubs also fosters the acceptance and development of video games, upcoming virtual reality applications, 360° video, and further interactive applications.

All this leads to specific but different requirements from applications to frameworks, service platforms, and networks. For each service, users desire special service criteria, such as smooth interactivity, fast downloads, high availability, or extensive content. Such requirements can usually be summarized under the term Quality of Experience (QoE), i.e., the overall satisfaction of a user with the system currently in use.

On the technical side, this opens up new paths towards analytics, big data, and automated machine learning techniques. Today, analyzing data to understand QoE touches at least one of these topics. In the era of big data and dynamic networks, QoE is still looking for its place, and good solutions are in high demand.

The research community has recognized QoE as a new discipline. Many new research challenges are being investigated, such as the realization of QoE in applications and communication networks. This is primarily due to the explicitly non‐technical definition in terms of the subjective satisfaction of the end user. Today, QoE research overlaps with many existing fields and brings new challenges to the surface, such as technical solutions for QoE monitoring in communication networks; the development of new services and applications based on human perception and QoE; the development and evaluation of improved QoE frameworks.

One such new research challenge is QoE Management with its integration in applications and communication networks. Although it is an increasingly relevant topic, there is little direct research that deals with the integration in the management of a service or a network. This Special Issue is devoted to the latest advances and challenges in analysis, design, modeling, measurement, and performance evaluation of QoE and QoE‐oriented metrics.

In total, seven articles were accepted from three different areas: (A) QoE Monitoring, (B) Video Streaming, and (C) Unified Communications. In “Impact of VNF Placements on QoE Monitoring in the Cloud”, Dinh‐Xuan et al. discuss challenges for QoE Monitoring. Over the past decade, Internet services have evolved tremendously, and the emergence of cloud computing has revolutionized the Internet ecosystem by providing the users with everything as a service. Cloud computing provides users with an arbitrary type of service: from entertainment services, like video streaming and cloud gaming, to office services, like Web‐based word processors and office suites. Equipped with only a thin client, customers can access their applications from anywhere, enjoy the best user experience, and take advantages of a scalable cloud.

However, a high demand for cloud services also poses challenges to network operators who want to maintain their quality of the provisioned services and retain the prospective customers in a competitive market. If the users experience a low service quality—for example, if a video stream is frequently interrupted—users may stop using the service and search for alternatives, leading to a decrease in revenue for the service provider. Thus, now more than ever, operators need to be aware of what users expect from their services. In the paper by Dinh‐Xuan et al., the authors design a virtual network function (VNF) using deep packet inspection and an algorithm to estimate the video buffer on the client to detect stalling events. From that point, they are able to measure the QoE based on a predefined model. To evaluate the performance of the VNF, the authors set up a testbed with the VNF deployed in a commercial cloud at different points of presence. They then evaluate the level of accuracy of the QoE estimation depending in relation to the concrete VNF placement.

The papers “An Objective and Subjective Quality Assessment Study of Passive Gaming Video Streaming” by Barman et al., “Improving Video QoE with IP‐over‐ICN” by Xylomenos et al., “Dissecting the Performance of YouTube Video Streaming in Mobile Networks” by Schwind et al., “Game of Protocols: Is QUIC Ready for Prime Time Streaming?” by Arisu et al., and “MUSLIN: A QoE‐Aware CDN Resources Provisioning and Advertising System for Cost‐Efficient Multi‐Source Live Streaming” by Da Silva et al. focus on video streaming in the context of QoE.

Barman et al. examine video streaming dedicated to gaming, such as Twitch and YouTube Gaming. For the continued success of such services, it is important that the QoE remains high, which is usually assessed using subjective and objective video quality assessment methods. The results in the paper indicate that Video Multi‐Method Assessment Fusion (VMAF) best predicts subjective video quality ratings, while Naturalness Image Quality Evaluator (NIQE) turns out to be a promising alternative as a no‐reference metric in some scenarios. In the following articles, QoE for video streaming is examined in different scenarios. Xylomenos et al. deal with video streaming in information‐centric networking. The work shows the results of the POINT research project. Schwind et al. perform a study in which over 1500 videos were streamed in mobile networks across Europe. Network and application data were collected and evaluated. The transport protocol QUIC was analyzed by Arisu et al., and Da Silva et al. specify a system for provisioning a video streaming architecture.

The final part of the Special Issue addresses Unified Communications platforms. In a survey, Barakovic et al. describe influencing parameters and key data for Unified Communications with regards to QoE. A deep and comprehensive understanding of the influencing factors and their impact on QoE for a given service is an essential precondition for successful QoE management with the overall goal of prominently optimizing end‐user QoE, while making efficient use of network resources and maintaining a satisfied user base.

All in all, the works collected here cover a broad range of QoE research questions in relation to network management. This clearly indicates the importance this topic has and why it is suggested to put questions of QoE in network management under consideration in the future. The guest editors would like to thank the authors and also the reviewers for their great work. The Special Issue is the outcome of many efforts towards the research topic.

Special Issue website: https://ijnm-qoe.com Journal website: https://onlinelibrary.wiley.com/journal/10991190

近年来，在多媒体环境和交互式环境中，Internet应用程序的异构性和可变性已大大增加。如今，视频流负责Internet中的大部分流量。互联网电话和视频会议系统已成为我们日常生活的一部分。同时，物联网努力超越对设备数量的任何先前期望。智能手机和平板电脑对网络的需求不断增长。此外，各种类型的媒体中心的兴起也促进了视频游戏，即将到来的虚拟现实应用程序，360°视频以及其他交互式应用程序的接受和开发。

所有这些导致从应用程序到框架，服务平台和网络的特定但不同的要求。对于每种服务，用户都需要特殊的服务标准，例如流畅的交互性，快速下载，高可用性或内容广泛。通常可以用术语“体验质量”（QoE）来概括此类要求，即用户对当前使用的系统的总体满意度。

在技​​术方面，这为分析，大数据和自动化机器学习技术开辟了新途径。如今，分析数据以了解QoE至少涉及这些主题之一。在大数据和动态网络时代，QoE仍在寻找自己的位置，对好的解决方案的需求也很高。

研究界已经将QoE视为一门新学科。正在研究许多新的研究挑战，例如在应用程序和通信网络中实现QoE。这主要是由于在最终用户的主观满意度方面存在明显的非技术性定义。如今，QoE研究与许多现有领域重叠，并带来了新的挑战，例如通信网络中QoE监控的技术解决方案；基于人类感知和QoE的新服务和应用程序的开发；改进的QoE框架的开发和评估。

QoE Management及其在应用程序和通信网络中的集成是此类新的研究挑战。尽管这是一个越来越重要的话题，但是很少有直接研究涉及服务或网络管理中的集成。本特刊专门介绍QoE和面向QoE的指标的分析，设计，建模，测量和性能评估方面的最新进展和挑战。

总共从三个不同领域接受了七篇文章：（A）QoE监视，（B）视频流和（C）统一通信。在“ VNF部署对云中QoE监控的影响”中”，Dinh‐Xuan等。讨论QoE监控的挑战。在过去的十年中，Internet服务已经发生了巨大的发展，而云计算的出现通过为用户提供一切即服务的服务，彻底改变了Internet生态系统。云计算为用户提供了任意类型的服务：从娱乐服务（例如视频流和云游戏）到办公服务（例如基于Web的文字处理器和办公套件）。客户仅配备瘦客户端，就可以从任何地方访问他们的应用程序，享受最佳的用户体验，并利用可扩展的云。

但是，对云服务的高需求也给想要保持所提供服务的质量并在竞争激烈的市场中保留潜在客户的网络运营商提出了挑战。如果用户体验到较低的服务质量（例如，视频流频繁中断），则用户可能会停止使用该服务并搜索替代方案，从而导致服务提供商的收入减少。因此，运营商现在比以往任何时候都需要意识到用户对他们的服务的期望。在Dinh-Xuan等人的论文中，作者设计了使用深度包检测的虚拟网络功能（VNF）和一种算法来估计客户端上的视频缓冲区以检测停顿事件。从那时起，他们能够基于预定义的模型来测量QoE。为了评估VNF的性能，作者建立了一个测试平台，将VNF部署在商业云的不同位置。然后，他们根据具体的VNF放置情况评估QoE估算的准确性水平。

Barman等人的论文“无源游戏视频流的客观和主观质量评估研究”，Xylomenos等人的论文“通过IP-over-ICN改善视频QoE”，“剖析移动网络中YouTube视频流的性能” ”，由Schwind等人撰写，“协议游戏：QUIC准备好进行黄金时间流传输了吗？” Da Silva等人撰写的“ MUSLIN：一种具有成本效益的多源实时流媒体的QoE感知CDN资源供应和广告系统”。在QoE的背景下专注于视频流。

Barman等。检查专用于游戏的视频流，例如Twitch和YouTube游戏。对于此类服务的持续成功，重要的是要保持较高的QoE，通常使用主观和客观的视频质量评估方法对其进行评估。论文中的结果表明，视频多方法评估融合（VMAF）可以最好地预测主观视频质量等级，而自然图像质量评估器（NIQE）在某些情况下被证明是一种有望成为无参考指标的替代方法。在以下文章中，将在不同情况下检查视频流的QoE。Xylomenos等。在以信息为中心的网络中处理视频流。该作品显示了POINT研究项目的结果。Schwind等。进行了一项研究，在整个欧洲的移动网络中流过1500多个视频。收集和评估网络和应用程序数据。Arisu等人和Da Silva等人分析了运输协议QUIC。指定用于配置视频流架构的系统。

特刊的最后部分介绍了统一通信平台。在一项调查中，Barakovic等人。描述有关QoE的影响统一通信的参数和关键数据。对特定服务的影响因素及其对QoE的影响的深刻而全面的了解是成功进行QoE管理的基本前提，其总体目标是显着优化最终用户QoE，同时有效利用网络资源并保持满意的用户基础。

总而言之，这里收集的作品涵盖了与网络管理有关的广泛的QoE研究问题。这清楚地表明了此主题的重要性，以及为什么建议在将来考虑在网络管理中考虑QoE的问题。特邀编辑要感谢作者和审稿人的出色工作。特刊是针对该研究课题进行的许多努力的成果。

特刊网站：https://ijnm-qoe.com期刊网站：https://onlinelibrary.wiley.com/journal/10991190