Quality of Experience describes the "degree of delight or annoyance of the user of an application or service". This holistic concept not only covers aspects of the service itself but also of the network through which the service is delivered. It has emerged from the classical Quality of Service (QoS) concept in telecommunications and bridges to the user experience concept from human-computer interaction (HCI), as well as (multi)media research, social psychology, cognitive science, engineering, and economics.
Quality of Experience research at the chair is focused on the following topics:
Fundamentals QoE fundamentals are researched at the chair to bring forward the theoretical foundations of the QoE concept. This includes the design of QoE studies and the validation of new QoE metrics. Moreover, the research is focused on measurements and modeling of QoE beyond single sessions (multiple sessions, multiple episodes).
Streaming Applications A major research field at the chair are streaming applications. This includes HTTP adaptive video streaming (e.g., YouTube, Netflix) and music streaming (e.g., Spotify). The goal is to model these services and their traffic in order to eventually derive QoE relationships and improve the QoE by network management.
Interactive Applications Interactive applications, such as web applications and real-time video conferencing, pose new challenges to network and service providers. The research goal is to understand and improve the delivery of such services through the network in order to improve the QoE for end users.
Enterprise/Business Applications Apart from classical end-user multimedia Internet applications, such as video streaming and web browsing, research is conducted to transfer the QoE concept to enterprise and business applications, such as web-based office suite, remote desktop, or networked business software.
Gaming Network-based video games are an emerging market with high network requirements, e.g., in terms of bandwidth and latency. The chair researches how to investigate the QoE in games with the help of subjective studies in order to develop QoE models.
User Behavior There is a complex interplay between QoE and user behavior, such that QoE influences the user behavior, user behavior might interact with the service (e.g., in terms of engagement), which again has an impact on the QoE. The goal is to understand and leverage this interplay, e.g., in order to infer the QoE from observed user behavior.
Application Layer QoS refers to technical performance metrics, which have a high correlation to QoE, but can be obtained through network or application measurements. These technical performance metrics can be used for QoE-aware network management cycles, which strive to improve these metrics by network management.
Fundamentals The chair investigates which measurement methodologies can be applied to obtain application layer QoS metrics both from the network traffic or within the application. For this, different application types are considered.
Streaming Applications HTTP adaptive video streaming (e.g., YouTube, Netflix) is well understood and its most important applications layer QoS metrics (stalling, visual quality, initial delay) were identified in numerous subjective studies. Prototype monitoring systems are developed and evaluated (e.g., YoMoApp), which show the technical feasibility of the approach.
Cloud Applications Cloud applications can show a high latency if they are placed far away from the user. The idea of edge clouds allows to place applications closer to the user. However, an additional overhead of placement and migration is introduced. It has to be investigated how the performance of cloud applications in an edge cloud system can be monitored to eventually improve the QoE for end users.
Researchers of the chair investigate not only how to use crowdsourcing for subjective QoE studies, but are also concerned about the general process of crowdsourcing, which is an emerging, future-oriented way of labor. The basic characteristics of crowdsourcing include flexible and small (micro) tasks with fast completion times, which are executed by a group of (anonymous) workers in remote work via the Internet. Thereby, both specialized and naive crowds are employed to leverage the so called "wisdom of the crowd".
Fundamentals The major focus is on how to efficiently apply crowdsourcing in QoE studies as well as in enterprise environments. This particularly involves aspects such as worker recruitment and motivation, as well as quality assessment of the crowdsourced tasks.
Workflow Optimization The goal is to optimize the crowdsourcing workflow for both the workers as well as the employer. This includes the task design and task description, such that workers can understand the tasks, complete them, and earn money. On the other hand, this also includes reliability checks and results analysis, such that the employer receives trustworth results from the crowdsourcing campaign.
Crowdsensing The omnipresence of smartphones and the increase of IoT sensor devices gives rise to a merger of crowdsourcing with classical (mobile) sensing. Crowdsensing research is focused on the mobility of crowdworkers, as well as the interplay in hybrid crowdsensing/IoT solutions.
Group-based communication is a new communication paradigm, which emerged with the rise of mobile messaging applications (MMAs). In communication groups messages do not necessarily have only a single receiver, but often they are sent to many users, which poses a high load on networks. It is necessary to understand and model this new communication paradigm to optimize the networks for such traffic.
Mobile Messenger The ubiquitous and fast-paced communication through MMAs (e.g., WhatsApp) and the multiplication of sent messages (not only text, but also images and videos), which have to be transmitted to many receivers in a communication group, generate huge amounts of data. Different mechanisms are researched to optimize the network traffic based on a good understanding of the underlying communication group, e.g., by caching at the network edge.
(since April 2014)
This project focuses on designing and evaluating new mechanisms in micro tasking platforms to improve the basic concepts with respect to the interests of provider, employer and worker.
(May 2019 - April 2021)
This project focusses on the relationship between the perceived quality of the performance of business applications by the employees and the technical performance data of such applications.
(since December 2017 )
What's Up is an interdisciplinary project together with psychologists of the University of Tübingen to analyze the communication of depressive children and adolescents in WhatsApp. With the help of WhatsAnalyzer, an early-warning system for depressive phases will be developed, which can effectively be used in the treatment of depression.
Hirth, M., Borchert, K., Allendorf, F., Metzger, F., Hoßfeld, T.: Crowd-based Study of Gameplay Impairments and Player Performance in DOTA 2. 4th Internet-QoE Workshop: QoE-based Analysis and Management of Data Communication Networks (Internet-QoE'19). , Los Cabos, Mexico (2019).
Hirth, M., Steurer, F., Borchert, K., Dubiner, D.: Task Scheduling on Crowdsourcing Platforms for Enabling Completion Time SLAs. 31st International Teletraffic Congress (ITC). , Budapest, Hungary (2019).
Wassermann, S., Casas, P., Seufert, M., Wamser, F.: On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements. 12th IFIP Wireless and Mobile Networking Conference (WMNC). , Paris, France (2019).
Borchert, K., Hirth, M., Stellzig-Eisenhauer, A., Kunz, F.: Crowd-based Assessment of Deformational Cranial Asymmetries. International Workshop on Crowd-Powered e-Services (CROPS). , Trondheim, Norway (2019).
Seufert, M., Wassermann, S., Casas, P.: Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-aware Traffic Management. IEEE Communications Letters. 23, 1145-1148 (2019).