Leistungsmodellierung von mobilem Videostreaming

In the past two decades, there has been a trend to move from traditional television to Internet-based video services. With video streaming becoming one of the most popular applications in the Internet and the current state of the art in media consumption, quality expectations of consumers are increasing. Low quality videos are no longer considered acceptable in contrast to some years ago due to the increased sizes and resolution of devices. If the high expectations of the users are not met and a video is delivered in poor quality, they often abandon the service. Therefore, Internet Service Providers (ISPs) and video service providers are facing the challenge of providing seamless multimedia delivery in high quality. Currently, during peak hours, video streaming causes almost 58\% of the downstream traffic on the Internet. With higher mobile bandwidth, mobile video streaming has also become commonplace. According to the 2019 Cisco Visual Networking Index, in 2022 79% of mobile traffic will be video traffic and, according to Ericsson, by 2025 video is forecasted to make up 76% of total Internet traffic. Ericsson further predicts that in 2024 over 1.4 billion devices will be subscribed to 5G, which will offer a downlink data rate of 100 Mbit/s in dense urban environments. One of the most important goals of ISPs and video service providers is for their users to have a high Quality of Experience (QoE). The QoE describes the degree of delight or annoyance a user experiences when using a service or application. In video streaming the QoE depends on how seamless a video is played and whether there are stalling events or quality degradations. These characteristics of a transmitted video are described as the application layer Quality of Service (QoS). In general, the QoS is defined as "the totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service" by the ITU. The network layer QoS describes the performance of the network and is decisive for the application layer QoS. In Internet video, typically a buffer is used to store downloaded video segments to compensate for network fluctuations. If the buffer runs empty, stalling occurs. If the available bandwidth decreases temporarily, the video can still be played out from the buffer without interruption. There are different policies and parameters that determine how large the buffer is, at what buffer level to start the video, and at what buffer level to resume playout after stalling. These have to be finely tuned to achieve the highest QoE for the user. If the bandwidth decreases for a longer time period, a limited buffer will deplete and stalling can not be avoided. An important research question is how to configure the buffer optimally for different users and situations. In this work, we tackle this question using analytic models and measurement studies. With HTTP Adaptive Streaming (HAS), the video players have the capability to adapt the video bit rate at the client side according to the available network capacity. This way the depletion of the video buffer and thus stalling can be avoided. In HAS, the quality in which the video is played and the number of quality switches also has an impact on the QoE. Thus, an important problem is the adaptation of video streaming so that these parameters are optimized. In a shared WiFi multiple video users share a single bottleneck link and compete for bandwidth. In such a scenario, it is important that resources are allocated to users in a way that all can have a similar QoE. In this work, we therefore investigate the possible fairness gain when moving from network fairness towards application-layer QoS fairness. In mobile scenarios, the energy and data consumption of the user device are limited resources and they must be managed besides the QoE. Therefore, it is also necessary, to investigate solutions, that conserve these resources in mobile devices. But how can resources be conserved without sacrificing application layer QoS? As an example for such a solution, this work presents a new probabilistic adaptation algorithm that uses abandonment statistics for ts decision making, aiming at minimizing the resource consumption while maintaining high QoS. With current protocol developments such as 5G, bandwidths are increasing, latencies are decreasing and networks are becoming more stable, leading to higher QoS. This allows for new real time data intensive applications such as cloud gaming, virtual reality and augmented reality applications to become feasible on mobile devices which pose completely new research questions. The high energy consumption of such applications still remains an issue as the energy capacity of devices is currently not increasing as quickly as the available data rates. In this work we compare the optimal performance of different strategies for adaptive 360-degree video streaming.In den vergangenen zwei Jahrzehnten gab es einen starken Trend weg vom traditionellen Fernsehen hin zum Videostreaming über das Internet. Dabei macht Videostreaming zurzeit den größten Anteil des gesamten Internetverkehrs aus. Beim Herunterladen eines Internetvideos wird das Video vor dem Ausspielen in einem Puffer beim Client zwischengespeichert, um Netzfluktuationen zu kompensieren. Leert sich der Puffer, so muss das Video stoppen (Stalling), um Daten nachzuladen. Um dies zu verhindern, müssen Pufferstrategien und -Parameter optimal an Nutzerszenarien angepasst sein. Mit diesem Problem beschäftigen wir uns im ersten Kapitel dieser Arbeit unter Anwendung von Wartschlangenmodelle, numerische Simulationen und Messstudien. Zur Bewertung der Güte eines Videostreams nutzen wir ein Modell, das auf subjektiven Studien basiert. Mit HTTP Adaptive Streaming hat der Videoplayer die Fähigkeit, Videosegmente in einer an die Bandbreite angepasster Bitrate und somit auch angepasster Qualität anzufordern. Somit kann die Leerung des Puffers gebremst und Stalling verhindert werden. Allerdings hat neben Stalling auch die Videoqualität und die Anzahl der Qualitätswechsel Auswirkungen auf die Zufriedenheit der Zuschauer. Inwiefern diese Parameter optimiert werden können, untersuchen wir im zweiten Kapitel mit Hilfe von linearen und quadratischen Programmen sowie einem Warteschlangenmodell. Hierbei untersuchen wie auch die Fairness in Netzen mit mehreren Nutzern und 360-Grad Videos. Im dritten Kapitel untersuchen wir Möglichkeiten, Videostreaming ressourcenschonender zu gestalten. Hierzu untersuchen wir in einer Feldstudie die Möglichkeit Caches an WiFi-Hotspots einzusetzen und somit redundanten Verkehr zu reduzieren. Wir untersuchen das Verhalten von mobilen Videonutzern, indem wir eine Nutzerstudie auswerten. Außerdem stellen wir einen neuen Adaptionsalgorithmus vor, der abhängig vom Nutzerverhalten den Datenverbrauch und Stromverbrauch des Videostreams reduziert