Cognitive Video Streaming

Video-on-demand (VoD) streaming services are becoming increasingly popular due to their flexibility to allow users to access their favorite video contents anytime, anywhere from a wide range of access devices such as smart phones, computers and TV. The content providers rely on highly satisfied subscribers for revenue generation and there has been significant efforts in developing approaches to “estimate” the quality of experience (QoE) of VoD subscribers. But a key issue is that QoE is not defined, appropriate proxies needs to be found for QoE, via the streaming metrics (the quality of service (QoS) metrics) that are largely based on initial startup time, buffering delays, average bit rate and average throughput and other relevant factors such as the video content and user behavior and other external factors. The ultimate objective of the content provider is to elevate the QoE of all the subscribers at the cost of minimal network resources, such as hardware resources and bandwidth. We propose a cognitive video streaming strategy in order to ensure the QoE of subscribers while utilizing minimal network resources. The proposed cognitive video streaming architecture consists of an estimation module, a prediction module and an adaptation module. Then, we demonstrate the prediction module of the cognitive video streaming architecture through a play time prediction tool. For this purpose, the applicability of different machine learning algorithms such as k-nearest neighbor, neural network regression and survival models are experimented with; then, we develop an approach to identify the most relevant factors that contributed to the prediction. The proposed approaches are tested on data set provided by Comcast Cable

Quality of experience and access network traffic management of HTTP adaptive video streaming

The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on Twitch

Is it possible to predict moment-to-moment gameplay engagement based solely on game telemetry? Can we reveal engaging moments of gameplay by observing the way the viewers of the game behave? To address these questions in this paper, we reframe the way gameplay engagement is defined and we view it, instead, through the eyes of a game's live audience. We build prediction models for viewers' engagement based on data collected from the popular battle royale game PlayerUnknown's Battlegrounds as obtained from the Twitch streaming service. In particular, we collect viewers' chat logs and in-game telemetry data from several hundred matches of five popular streamers (containing over 100,000 game events) and machine learn the mapping between gameplay and viewer chat frequency during play, using small neural network architectures. Our key findings showcase that engagement models trained solely on 40 gameplay features can reach accuracies of up to 80% on average and 84% at best. Our models are scalable and generalisable as they perform equally well within- and across-streamers, as well as across streamer play styles.Comment: Version accepted for the Conference on the Foundations of Digital Games 2020 - Malt

Development and empirical testing of a game engagement scale : case r/Stopgaming

The thesis conceptualises gaming from leisurely and labour-like starting points and both measures and develops this perspective by pioneering a concept of game engagement. Putting forth this perspective is grounded by appeals to burnout in and out of professional contexts in the videogame industry, ludology and research on player experiences. These views coming together prompted a need to verify whether games are to be normatively held as engaging in only popular belief, or verifiably so in actuality. In so doing, both methodological and theoretical insight is provided. The engagement construct was adapted from the Utrecht Work Engagement Scale -9 (short form) and a survey study was conducted. Data was analysed using ordinal logistic regression and exploratory factor analysis. Results showed those not holding games dear to them may require substantial investment increases to reap adequate increases in engagement, if playtime is low, while a committed orientation towards gaming (in terms of subjective gamerhood and hours played) showed marked differences in engagement per incremental increase in playtime. These results are considered descriptive, rather than predictive. Future directions for game studies are suggested to uncover how players become disengaged and how rationalisation affects the gaming experience

Good gamers, good managers? A proof-of-concept study with Sid Meier’s Civilization

Human resource professionals increasingly enhance their assessment tools with game elements—a process typically referred to as “gamification”—to make them more interesting and engaging for candidates, and they design and use “serious games” that can support skill assessment and development. However, commercial, off-the-shelf video games are not or are only rarely used to screen or test candidates, even though there is increasing evidence that they are indicative of various skills that are professionally valuable. Using the strategy game Civilization, this proof-of-concept study explores if strategy video games are indicative of managerial skills and, if so, of what managerial skills. Under controlled laboratory conditions, we asked forty business students to play the Civilization game and to participate in a series of assessment exercises. We find that students who had high scores in the game had better skills related to problem-solving and organizing and planning than the students who had low scores. In addition, a preliminary analysis of in-game data, including players’ interactions and chat messages, suggests that strategy games such as Civilization may be used for more precise and holistic “stealth assessments,” including personality assessments

Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-aware Traffic Management

International audienceThe concept of Quality of Experience (QoE) of Internet services is widely recognized by service providers and network operators. They strive to deliver the best experience to their customers in order to increase revenues and avoid churn. Therefore, QoE is increasingly considered as an integral part of the reactive traffic management cycle of network operators. Additionally, QoE also constitutes a cycle of its own, which includes the user behavior and the service requirements. This work describes this QoE cycle, which is not widely taken into account yet, discusses the interactions of the two cycles, and derives implications towards an improved and proactive QoE-aware traffic management. A showcase on how network operators can obtain hints on the change of network requirements from detecting user behavior in encrypted video traffic is also presented in this paper

Moment-to-moment engagement prediction through the eyes of the observer : PUBG streaming on Twitch

Is it possible to predict moment-to-moment gameplay engagement based solely on game telemetry? Can we reveal engaging moments of gameplay by observing the way the viewers of the game behave? To address these questions in this paper, we reframe the way gameplay engagement is defined and we view it, instead, through the eyes of a game’s live audience.We build prediction models for viewers’ engagement based on data collected from the popular battle royale game PlayerUnknown’s Battlegrounds as obtained from the Twitch streaming service. In particular, we collect viewers’ chat logs and in-game telemetry data from several hundred matches of five popular streamers (containing over 100, 000 game events) and machine learn the mapping between gameplay and viewer chat frequency during play, using small neural network architectures. Our key findings showcase that engagement models trained solely on 40 gameplay features can reach accuracies of up to 80% on average and 84% at best. Our models are scalable and generalisable as they perform equally well within- and across-streamers, as well as across streamer play styles.peer-reviewe

Interference-aware multipath video streaming in vehicular environments

The multipath transmission is one of the suitable transmission methods for high data rate oriented communication such as video streaming. Each video packets are split into smaller frames for parallel transmission via different paths. One path may interfere with another path due to these parallel transmissions. The multipath oriented interference is due to the route coupling which is one of the major challenges in vehicular traffic environments. The route coupling increases channel contention resulting in video packet collision. In this context, this paper proposes an Interference-aware Multipath Video Streaming (I-MVS) framework focusing on link and node disjoint optimal paths. Specifically, a multipath vehicular network model is derived. The model is utilized to develop interference-aware video streaming method considering angular driving statistics of vehicles. The quality of video streaming links is measured based on packet error rate considering non-circular transmission range oriented shadowing effects. Algorithms are developed as a complete operational I-MVS framework. The comparative performance evaluation attests the benefit of the proposed framework considering various video streaming related metrics

The Basic Needs in Games (BANG) Model of Video Games and Mental Health: Untangling the Positive and Negative Effects of Games with Better Science

How do video games affect mental health? Despite decades of research and widespread interest from policymakers, parents, and players, in most cases the best answer we have is: it depends. I argue that our limited success stems largely from (1) a lack of theories that explain more than small portions of the varied evidence base, and (2) methodological limitations related to measurement, self-report data, questionable research practices, and more. In this thesis, I present the Basic Needs in Games (BANG) model. Building upon self-determination theory, BANG offers a novel theoretical account that provides mechanisms for both short- and long-term effects, positive and negative, resulting from quality or quantity of gaming. Under BANG, the primary mechanism through which games impact mental health is via need satisfaction and frustration: the extent to which both games, and players’ life in general, provide experiences of control and volition (autonomy), mastery and growth (competence), and connection and belonging (relatedness). To generate BANG, I conducted semi-structured interviews, finding that need-frustrating experiences within games have important effects on player behavior, likelihood of continuing play, and expectations for future experiences (Study 1). In a mixed-method survey, I show that some—but not all—players are successful in compensating for frustrated needs in daily life by playing games (Study 2). These findings informed the validation of the the Basic Needs in Games Scale (BANGS), as previous instruments either did not measure need frustration or were not designed for gaming contexts. Across 1400 participants and various validity analyses, I show that the questionnaire is suitable for wide-ranging use (Study 3). Finally, I collected 12 weeks of digital trace data using a novel method of monitoring the Xbox network, and combined this with 6 biweekly surveys measuring need satisfaction and frustration alongside three mental health constructs (Study 4). Across 2000 responses (n = 400), I find partial support for BANG: there is strong evidence to rule out a meaningful relationship between playtime and subsequent mental health. However, players who felt more need satisfaction than usual in games also reported higher than usual need satisfaction in general, which in turn related to better mental health. My results help push the field beyond simplified notions of playtime by offering a framework that can systematically account for a wide variety of observed gaming effects. I hope that this work can serve as both a call to action and an illustrative example of how games research can be more productive