Online video games and cloud gaming are rapidly growing in pervasiveness. Their resource demands can put significant stress on the global communication infrastructure. And network conditions are amongst the chief factors that influence one’s enjoyment while playing games. This makes it imperative for video games to be considered for network dimensioning, server placement or protocol development. For that reason, in this work we provide an introduction to the technical aspects of video games in general and of their network aspects in particular. This understanding forms the basis for a rich taxonomy of factors that influence and provide context to a video game’s Quality of Service (QoS) and Quality of Experience (QoE). The taxonomy covers influence factors from all aspects involved in a video game, from the subjective player and game influence factors to the system and networking influence factors. Finally, this work gives an overview of conducted and ongoing research as well as future research opportunities while taking into account lessons learned from past approaches.

Subjective speech quality assessment has traditionally been carried out in laboratory environments under controlled conditions. With the advent of crowdsourcing platforms tasks, which need human intelligence, can be resolved by crowd workers over the Internet. Crowdsourcing also offers a new paradigm for speech quality assessment, promising higher ecological validity of the quality judgments at the expense of potentially lower reliability. This paper compares laboratory-based and crowdsourcing-based speech quality assessments in terms of comparability of results and efficiency. For this purpose, three pairs of listening-only tests have been carried out using three different crowdsourcing platforms and following the ITU-T Recommendation P.808. In each test, listeners judge the overall quality of the speech sample following the Absolute Category Rating procedure. We compare the results of the crowdsourcing approach with the results of standard laboratory tests performed according to the ITU-T Recommendation P.800. Results show that in most cases, both paradigms lead to comparable results. Notable differences are discussed with respect to their sources, and conclusions are drawn that establish practical guidelines for crowdsourcing-based speech quality assessment.

On video streaming platforms, users expect a high Quality of Experience (QoE). In contrast, service and content providers aim at high User Engagement, most notably because their revenue is usually dependent on it. In order to satisfy users, it is critical to know how QoE is related to the User Engagement. However, no model for this relation exists yet. Current approaches to managing QoE are usually based on traffic analysis. However, this will become more difficult in the future since the encryption of Internet traffic progresses. In this paper, we present an approach to bringing QoE and User Engagement together with video streaming as the use case. We do this by fitting existing measurement data of User Engagement to obtain a model. Furthermore, we extend the existing queuing model for QoE and investigate the correlation between QoE and User Engagement in a simulation. Hereby, we model different scenarios where we quantify network bandwidth and video requirements. Our results show that there is a strong correlation between QoE and User Engagement. Additionally, we observe that simple buffer policies, which do not rely on buffer information, can still perform well. These revelations open the way for new approaches to QoE Management in the future Internet.

Video streaming provided by Over-The-Top (OTT) service providers through a cellular network is a common usage scenario for many people today. While video streaming will work reasonably well in a stationary scenario, several issues arise for mobile users. For instance, travelling through short areas without cellular coverage, such as an automobile tunnel, will often result in quality degradation or video stalling. To combat this, this paper provides an analytical investigation of the video quality degradation problem as it is experienced by mobile users and suggests a context-aware HTTP Adaptive Streaming (HAS) strategy to prevent stalling and minimize the impact on Quality of Experience (QoE). This provides a solution that can completely prevent stalling when appropriate context information (such as positional information from satellite navigation) is present. The collected evaluation results encourage further research on how context-awareness can be exploited to further enhance video service provisioning by OTT service providers.

Video sharing services like YouTube have become very popular which consequently results in a drastic shift of the Internet traffic statistic. When transmitting video content over packet based networks, stringent quality of service (QoS) constraints must be met in order to provide the comparable level of quality to a traditional broadcast television. However, the packet transmission is influenced by delays and losses of data packets which can have devastating influence on the perceived quality of the video. Therefore, we conducted an experimental evaluation of HTTP based video transmission focusing on how they react to packet delay and loss. Through this analysis we investigated how long video playback is stalled and how often re-buffering events take place. Our analysis revealed threshold levels for the packet delay, packet losses and network throughput which should not be exceeded in order to preserve smooth video transmission.

This paper takes an exploratory look on control plane signaling in a mobile cellular core network. In contrast to most contributions in this field, our focus does not lie on the wireless or user-oriented parts of the network, but on signaling in the core network. In an investigation of core network data we take a look at statistics related to GTP tunnels and their signaling. Based on the results thereof we propose a definition of load at the GGSN and create an initial load queuing model. We find signs of user devices putting burden on the core network through their behavior.