Delay Sensitivity Classification of Cloud Gaming Content

Cloud Gaming is an emerging service that catches growing interest in the research community as well as industry. While the paradigm shift from a game execution on clients to streaming games from the cloud offers a variety of benefits, the new services also require a highly reliable and low latency network to achieve a satisfying Quality of Experience (QoE) for its users. Using a cloud gaming service with high latency would harm the interaction of the user with the game, leading to a decrease in playing performance and thus frustration of players. However, the negative effect of delay on gaming QoE depends strongly on the game content. At a certain level of delay, a slow-paced card game is typically not as delay sensitive as a shooting game. For optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the impact of the game content. This paper contributes to a better understanding of the delay impact on QoE for cloud gaming applications by identifying game characteristics influencing the delay perception of users. In addition, an expert evaluation methodology to quantify these characteristics, as well as a delay sensitivity classification based on a decision tree is presented. The ratings of 14 experts for the quantification indicated an excellent level of agreement which demonstrates the reliability of the proposed method. Additionally, the decision tree reached an accuracy of 86.6 % on determining the delay sensitivity classes which were derived from a large dataset of subjective input quality ratings during a series of experiments.Comment: Accepted In International Workshop on Immersive Mixed and Virtual Environment Systems 2020. ACM, Istanbul, Turke

Towards the Need Satisfaction in Gaming: A comparison of different gaming platforms

Abstract—Recent advances in Virtual Reality (VR) technologies have resulted in a wider availability of Head Mounted Displays (HMDs). However, it is still unclear if VR gaming offers a substantial added value to players. For this reason a comparison of gaming experiences on VR HMD to those on mobile and PC, two other popular gaming platforms, is performed by conducting a user study via two games available on all three platforms. We explore the QoE of gaming by investigating momentous dimensions using the Player Experience of Need Satisfaction (PENS) questionnaire. The results show higher Presence and Autonomy obtained by using HMD when compared to the two other platforms. However, these factors alone did not improve the Overall Quality. To take advantage of the new technology, satisfaction of all psychological needs, especially Competency, must be assured

DEMI : deep video quality estimation model using perceptual video quality dimensions

Existing works in the field of quality assessment focus separately on gaming and non-gaming content. Along with the traditional modeling approaches, deep learning based approaches have been used to develop quality models, due to their high prediction accuracy. In this paper, we present a deep learning based quality estimation model considering both gaming and non-gaming videos. The model is developed in three phases. First, a convolutional neural network (CNN) is trained based on an objective metric which allows the CNN to learn video artifacts such as blurriness and blockiness. Next, the model is fine-tuned based on a small image quality dataset using blockiness and blurriness ratings. Finally, a Random Forest is used to pool frame-level predictions and temporal information of videos in order to predict the overall video quality. The light-weight, low complexity nature of the model makes it suitable for real-time applications considering both gaming and non-gaming content while achieving similar performance to existing state-of-the-art model NDNetGaming. The model implementation for testing is available on GitHub

QoE management of multimedia streaming services in future networks : a tutorial and survey

No embargo require

QUALINET white paper on definitions of Immersive Media Experience (IMEx)

With the coming of age of virtual/augmented reality and interactive media, numerous definitions, frameworks, and models of immersion have emerged across different fields ranging from computer graphics to literary works. Immersion is oftentimes used interchangeably with presence as both concepts are closely related. However, there are noticeable interdisciplinary differences regarding definitions, scope, and constituents that are required to be addressed so that a coherent understanding of the concepts can be achieved. Such consensus is vital for paving the directionality of the future of immersive media experiences (IMEx) and all related matters. The aim of this white paper is to provide a survey of definitions of immersion and presence which leads to a definition of immersive media experience (IMEx). The Quality of Experience (QoE) for immersive media is described by establishing a relationship between the concepts of QoE and IMEx followed by application areas of immersive media experience. Influencing factors on immersive media experience are elaborated as well as the assessment of immersive media experience. Finally, standardization activities related to IMEx are highlighted and the white paper is concluded with an outlook related to future developments

Modelle zur Vorhersage des Nutzungserlebens von Cloud-Gaming-Diensten

The gaming industry is one of the largest in the entertainment markets for the past several decades and is steadily growing with the introduction of emerging technologies such as hardware video encoding and the new generation of broadband cellular networks, 5G. With these advancements, a new gaming paradigm called cloud gaming has emerged that makes gaming possible at any time, on any device, and at any place. Cloud gaming shifts the heavy computational tasks such as rendering to the cloud resources and streams a compressed video of players' gameplay back to the client in real-time. Similar to other telecommunication services, cloud gaming is prone to network and compression degradations such as blockiness, blurring, and network latency. These degradations could negatively affect the Quality of Experience (QoE) of users. Therefore, it is of high interest for service and network providers to measure and monitor the QoE of cloud gaming services to potentially improve the satisfaction of their customers. The present thesis aims at the development of a gaming quality model to predict the gaming QoE of players that could be used for planning the network service or quality monitoring of cloud gaming services. The model is developed following a modular structure approach that keeps the different types of impairment separately. Such a modular structure allows developing a sustainable model as each component can be updated by advances in that specific research area or technology. The gaming quality model takes into account two modules of video quality and input quality. The latter considers the interactivity aspects of gaming. The video quality module offers a series of models that differ depending on the level of access to the video stream information, allowing high flexibility for service providers regarding the positions of measuring points within their system. Before the development of the video quality module, multiple state-of-the-art image and video quality models are evaluated with gaming content. Results revealed a poor performance of No-Reference (NR) models. Thus, a special focus was given to the development of NR models for gaming content. In sum, two planning models, one bitstream model, and three NR models were developed. The models cover typical video compression as well as transmission errors. For their development, either a direct modeling approach or a multidimensional approach was used. The latter approach allows getting insight into diagnostic information of causes for impaired video quality. Among the NR models, two deep learning-based models are proposed that outperform the well-known traditional Full-Reference and NR image/video quality metrics on gaming content. In order to consider the interactivity aspects of gaming, in addition to the video related impairment factors, the impact of network parameters such as delay and packet loss was assessed. To further increase the accuracy of the proposed gaming quality model, a classification of video games according to their sensitivity towards delay and frameloss, as well as video complexity, was proposed. Parts of the core model resulted in the ITU-T Rec. G.1072 that represents a planning model predicting the QoE of cloud gaming services. In summary, the main contributions of the thesis are (1) creation of multiple image/video and cloud gaming quality datasets, (2) development of a gaming video classification, and (3) development of a series of gaming QoE models to predict the gaming QoE depending on the level of access to the video stream information.Die Videospielebranche ist seit vielen Jahrzehnten einer der größten Unterhaltungsmärkte und wächst stetig mit der Einführung neuer Technologien wie Hardware-Videokodierung und der neuen Generation von Breitband-Mobilfunknetzen, 5G. Mit diesen Fortschritten ist ein neues Spielparadigma namens Cloud Gaming entstanden, das das Spielen jederzeit, auf jedem Gerät und an jedem Ort ermöglicht. Cloud Gaming verlagern die umfangreichen Rechenaufgaben wie das Rendern auf die Cloud-Ressourcen und streamt ein komprimiertes Video der Spielszenen von Spielern in Echtzeit an den Client zurück. Ähnlich wie bei anderen Telekommunikationsdiensten ist Cloud Gaming anfällig für Verschlechterungen wie Blockbildung, Unschärfe und Netzwerklatenz, die durch Übertragungsnetzwerke und Komprimierungen entstehen. Diese Verschlechterungen können sich negativ auf das Nutzungserleben (Quality of Experience, QoE) der Benutzer auswirken. Daher ist es für Dienst- und Netzwerkanbieter von großem Interesse, die QoE von Cloud-Gaming-Diensten zu messen und zu überwachen, um möglicherweise die Zufriedenheit ihrer Kunden zu verbessern. Die vorliegende Arbeit zielt auf die Entwicklung eines Gaming-Qualitätsmodells zur Vorhersage der Gaming-QoE von Spielern ab, das zur Planung des Netzwerkdienstes oder zur Qualitätsüberwachung von Cloud-Gaming-Diensten verwendet werden kann. Das Modell wurde nach einem modularen Strukturansatz entwickelt, bei dem die verschiedenen Arten von Beeinträchtigungen getrennt voneinander behandelt werden. Eine solche modulare Struktur ermöglicht die Entwicklung eines nachhaltigen Modells, da jede Komponente durch Fortschritte in diesem spezifischen Forschungsbereich oder dieser Technologie aktualisiert werden kann. Das Qualitätsmodell berücksichtigt zwei Module für die Videoqualität und Eingabequalität. Letzteres berücksichtigt die Interaktivitätsaspekte des Spielens. Das Videoqualitätsmodul bietet eine Reihe von Modellen, die sich je nach Zugriff auf die Videostream-Informationen unterscheiden. Dies ermöglicht Dienstanbietern eine hohe Flexibilität hinsichtlich der Positionen der Messpunkte in ihrem System. Vor der Entwicklung des Videoqualitätsmoduls wurden mehrere moderne Bild- und Videoqualitätsmodelle mit Spielinhalten untersucht. Die Ergebnisse zeigten eine schlechte Leistung von No-Reference-Modellen. Ein besonderer Schwerpunkt lag daher auf der Entwicklung solcher Modelle für Spielinhalte. Insgesamt wurden zwei Planungsmodelle, ein Bitstream-Modell und drei No-Reference-Modelle entwickelt. Die Modelle decken typische Videokomprimierungs- sowie Übertragungsfehler ab. Für ihre Entwicklung wurde entweder ein direkter Modellierungsansatz oder ein mehrdimensionaler Ansatz verwendet. Der letztere Ansatz ermöglicht es, Einblicke in diagnostische Informationen über Ursachen für eine beeinträchtigte Videoqualität zu erhalten. Unter den No-Reference-Modellen werden zwei auf Deep-Learning basierende Modelle vorgeschlagen, die die bekannten traditionellen Full-Referenz- und No-Reference-Metriken für Bild- / Videoqualität bei Spielinhalten übertreffen. Um die Interaktivitätsaspekte des Spielens zu berücksichtigen, werden zusätzlich zu den videobezogenen Beeinträchtigungsfaktoren die Auswirkungen von Netzwerkparametern wie Verzögerung und Paketverlust untersucht. Um die Genauigkeit des vorgeschlagenen Spielqualitätsmodells weiter zu erhöhen, wird eine Klassifizierung von Videospielen nach ihrer Empfindlichkeit gegenüber Verzögerung und Frame-Verlusten sowie nach ihrer Videokomplexität vorgeschlagen. Teile des Kernmodells führten zur ITU-T Rec. G.1072, das ein Planungmodell darstellt, das die QoE für Cloud Gaming-Dienste vorhersagt. Zusammenfassend sind die Hauptbeiträge der Arbeit (1) die Erstellung mehrerer Datensätze der Messung von Video- und Cloud Gaming-Qualität, (2) die Entwicklung einer Klassifikation von Videospielen, und (3) die Entwicklung einer Reihe von Gaming-QoE-Modellen zur Vorhersage der Gaming-QoE in Abhängigkeit vom Zugriff auf Videostream-Informationen