The energy consumption of mobile devices is a growing concern. Smartphones improve
their capabilities faster than their batteries. Resource hungry applications like streaming
services consume a lot of energy. The wireless interface, which uses a lot of energy, is
empirically configured to provide a good performance for most applications. Adaptive
video streaming, which is a popular streaming application, inefficiently utilises the wireless
interface and causes traffic overhead. We develop an adaptation strategy, which utilises
viewing statistics to reduce the energy consumption and traffic overhead. Additionally, the
adaptation strategy optimises the QoS, to improve the users video streaming experience.
We test and evaluate our strategy, using a simulation, which simulates users in a mobility
scenario. We simulate users, who are watching multiple videos and can skip and abandon
theplayback. Weshowthatenergyandtrafficcanbesaved, withmostlynegligibleinfluence
on the QoS. Energy efficiency and traffic saving mostly describe a tradeoff. Different
contexts offer different opportunities to save either more energy or more traffic. Further,
we show, that throughput fluctuations are an important influence factor for our adaptation
strategy.

In this thesis, we design a Quality of Experience (QoE) fair adaptive streaming algorithm and a simulation to evaluate its performance. We propose a design with an external coordinator that assesses QoE for clients behind a bottleneck and tries to maximize the QoE while reducing stalling and low video quality playback. The simulation was written with simpy, a discrete event framework in Python. We simulated multiple clients with configured and random arrival times and assessed the performance of the design adaptation strategy in comparison to other throughput- respectively buffer-based adaptation strategies. We found that the adaptation strategy did not outperform the buffer-based strategy on fairness and quality level playback

In this paper, Web Real-Time Communication (WebRTC) monitoring tool will be implemented and by using this tool, it is possible to analyse Quality of Experience (QoE) of WebRTC video conferencing. Furthermore based on research of test result, a relationship between network layer parameters and application layer parameters will be find. This monitoring tool works on web browser of google Chrome, with ability to show up real time informations and it also allow users to storage their test data at least in local disk.

Die Computer- und Videospiel-Industrie wächst stetig weiter und erfreut sich immer größerer Beliebtheit. Insbesondere das Spielerverhalten und die Bindung der Spieler an ein Produkt ist für diese Industrie besonders interessant. Diese Arbeit befasst sich daher besonders mit dem Nutzerverhalten der Videospiel-Plattformen Steam und Twitch.tv. Es wird eine empirische Untersuchung der Spieler- und Zuschauerzahlen durchgeführt und unterschiedlichste Effekte auf die Spielerzahlen dieser Spiele analysiert. Im Zuge dieser Analyse wurden viele Faktoren gefunden, die eine eindeutige Auswirkung auf die Spielerzahlen der gemessenen Computerspiele haben. Allerdings konnten auch einige vermutete Effekte in dieser Messung nicht bestätigt werden.

Video streaming has become one of the most bandwidth-consuming services on the Internet. As in video streaming the video is played while being downloaded, this process is sensitive to variations in the available bandwidth. Especially outages during the transmission can lead to quality degradations or even so called stalling events, in case the playback stops, because the player ran out of data. Stalling has a massive influence on the Quality of Experience (QoE). Variations in the bandwidth can be compensated with buffering. But with a larger buffer, not only the initial waiting time, which denotes the time until a video starts playing, but also the overall bandwidth consumption rises, because a large quantity of the videos are aborted before they end. In numbers, about 40% of all viewed videos on YouTube were aborted within the first 30 seconds, 20% of the videos were played less than ten seconds [Fin+11]. When a video is aborted, all video in the buffer is lost and can be counted as ‘wasted’. This generates significant costs for both user and service-provider. Transmitting data costs money for the provider of the video service, for server capacity as well as for traffic. But the transmission can also induce costs for the user. On mobile plans, it is often paid for a fixed amount of traffic. More traffic equals higher prices. Additionally, transmitting data on mobile devices consumes energy and therefore reduces battery lifetime. Summing up, a compromise between buffering and user experience has to be found. In this work, we investigate two different video players and their behavior under realistic scenarios while being on the move, in typical commuting situations. Hereby, we focus on the effect of buffering on the playback. As the user experience is still a heavily discussed field, we obtain objective Quality of Service key numbers of the playback. These are then be used to derive QoE numbers using different methods.