@article{KraftSchleeStachetal.2020, author = {Kraft, Robin and Schlee, Winfried and Stach, Michael and Reichert, Manfred and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Hannemann, Ronny and Pryss, R{\"u}diger}, title = {Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain}, series = {Frontiers in Neuroscience}, volume = {14}, journal = {Frontiers in Neuroscience}, number = {164}, issn = {1662-453X}, doi = {10.3389/fnins.2020.00164}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200220}, year = {2020}, abstract = {The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.}, language = {en} } @misc{Sieber2013, type = {Master Thesis}, author = {Sieber, Christian}, title = {Holistic Evaluation of Novel Adaptation Logics for DASH and SVC}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-92362}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2013}, abstract = {Streaming of videos has become the major traffic generator in today's Internet and the video traffic share is still increasing. According to Cisco's annual Visual Networking Index report, in 2012, 60\% of the global Internet IP traffic was generated by video streaming services. Furthermore, the study predicts further increase to 73\% by 2017. At the same time, advances in the fields of mobile communications and embedded devices lead to a widespread adoption of Internet video enabled mobile and wireless devices (e.g. Smartphones). The report predicts that by 2017, the traffic originating from mobile and wireless devices will exceed the traffic from wired devices and states that mobile video traffic was the source of roughly half of the mobile IP traffic at the end of 2012. With the increasing importance of Internet video streaming in today's world, video content provider find themselves in a highly competitive market where user expectations are high and customer loyalty depends strongly on the user's satisfaction with the provided service. In particular paying customers expect their viewing experience to be the same across all their viewing devices and independently of their currently utilized Internet access technology. However, providing video streaming services is costly in terms of storage space, required bandwidth and generated traffic. Therefore, content providers face a trade-off between the user perceived Quality of Experience (QoE) and the costs for providing the service. Today, a variety of transport and application protocols exist for providing video streaming services, but the one utilized depends on the scenario in mind. Video streaming services can be divided up in three categories: Video conferencing, IPTV and Video-on-Demand services. IPTV and video-conferencing have severe real-time constraints and thus utilize mostly datagram-based protocols like the RTP/UDP protocol for the video transmission. Video-on-Demand services in contrast can profit from pre-encoded content, buffers at the end user's device, and mostly utilize TCP-based protocols in combination with progressive streaming for the media delivery. In recent years, the HTTP protocol on top of the TCP protocol gained widespread popularity as a cost-efficient way to distribute pre-encoded video content to customers via progressive streaming. This is due to the fact that HTTP-based video streaming profits from a well-established infrastructure which was originally implemented to efficiently satisfy the increasing demand for web browsing and file downloads. Large Content Delivery Networks (CDN) are the key components of that distribution infrastructure. CDNs prevent expensive long-haul data traffic and delays by distributing HTTP content to world-wide locations close to the customers. As of 2012, already 53\% of the global video traffic in the Internet originates from Content Delivery Networks and that percentage is expected to increase to 65\% by the year 2017. Furthermore, HTTP media streaming profits from existing HTTP caching infrastructure, ease of NAT and proxy traversal and firewall friendliness. Video delivery through heterogeneous wired and wireless communications networks is prone to distortions due to insufficient network resources. This is especially true in wireless scenarios, where user mobility and insufficient signal strength can result in a very poor transport service performance (e.g. high packet loss, delays and low and varying bandwidth). A poor performance of the transport in turn may degrade the Quality of Experience as perceived by the user, either due to buffer underruns (i.e. playback interruptions) for TCP-based delivery or image distortions for datagram-based real-time video delivery. In order to overcome QoE degradations due to insufficient network resources, content provider have to consider adaptive video streaming. One possibility to implement this for HTTP/TCP streaming is by partitioning the content into small segments, encode the segments into different quality levels and provide access to the segments and the quality level details (e.g. resolution, average bitrate). During the streaming session, a client-centric adaptation algorithm can use the supplied details to adapt the playback to the current environment. However, a lack of a common HTTP adaptive streaming standard led to multiple proprietary solutions developed by major Internet companies like Microsoft (Smooth Streaming), Apple (HTTP Live Streaming) and Adobe (HTTP Dynamic Streaming) loosely based on the aforementioned principle. In 2012, the ISO/IEC published the Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. As of today, DASH is becoming widely accepted with major companies announcing their support or having already implemented the standard into their products. MPEG-DASH is typically used with single layer codecs like H.264/AVC, but recent publications show that scalable video coding can use the existing HTTP infrastructure more efficiently. Furthermore, the layered approach of scalable video coding extends the adaptation options for the client, since already downloaded segments can be enhanced at a later time. The influence of distortions on the perceived QoE for non-adaptive video streaming are well reviewed and published. For HTTP streaming, the QoE of the user is influenced by the initial delay (i.e. the time the client pre-buffers video data) and the length and frequency of playback interruptions due to a depleted video playback buffer. Studies highlight that even low stalling times and frequencies have a negative impact on the QoE of the user and should therefore be avoided. The first contribution of this thesis is the identification of QoE influence factors of adaptive video streaming by the means of crowd-sourcing and a laboratory study. MPEG-DASH does not specify how to adapt the playback to the available bandwidth and therefore the design of a download/adaptation algorithm is left to the developer of the client logic. The second contribution of this thesis is the design of a novel user-centric adaption logic for DASH with SVC. Other download algorithms for segmented HTTP streaming with single layer and scalable video coding have been published lately. However, there is little information about the behavior of these algorithms regarding the identified QoE-influence factors. The third contribution is a user-centric performance evaluation of three existing adaptation algorithms and a comparison to the proposed algorithm. In the performance evaluation we also evaluate the fairness of the algorithms. In one fairness scenario, two clients deploy the same adaptation algorithm and share one Internet connection. For a fair adaptation algorithm, we expect the behavior of the two clients to be identical. In a second fairness scenario, one client shares the Internet connection with a large HTTP file download and we expect an even bandwidth distribution between the video streaming and the file download. The forth contribution of this thesis is an evaluation of the behavior of the algorithms in a two-client and HTTP cross traffic scenario. The remainder of this thesis is structured as follows. Chapter II gives a brief introduction to video coding with H.264, the HTTP adaptive streaming standard MPEG-DASH, the investigated adaptation algorithms and metrics of Quality of Experience (QoE) for video streaming. Chapter III presents the methodology and results of the subjective studies conducted in the course of this thesis to identify the QoE influence factors of adaptive video streaming. In Chapter IV, we introduce the proposed adaptation algorithm and the methodology of the performance evaluation. Chapter V highlights the results of the performance evaluation and compares the investigated adaptation algorithms. Section VI summarizes the main findings and gives an outlook towards QoE-centric management of DASH with SVC.}, subject = {DASH}, language = {en} } @article{BorchertSeufertGamboaetal.2020, author = {Borchert, Kathrin and Seufert, Anika and Gamboa, Edwin and Hirth, Matthias and Hoßfeld, Tobias}, title = {In Vitro vs In Vivo: Does the Study's Interface Design Influence Crowdsourced Video QoE?}, series = {Quality and User Experience}, volume = {6}, journal = {Quality and User Experience}, issn = {2366-0139}, doi = {10.1007/s41233-020-00041-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235586}, year = {2020}, abstract = {Evaluating the Quality of Experience (QoE) of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale. In general, most of these crowdsourcing studies either use, what we refer to, as an in vivo or an in vitro interface design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these interface designs vary widely, the results are often compared and generalized. In this work, we use a crowdsourcing study to investigate the influence of three interface design alternatives, an in vitro and two in vivo designs with different levels of interactiveness, on the perceived video QoE. Contrary to our expectations, the results indicate that there is no significant influence of the study's interface design in general on the video experience. Furthermore, we found that the in vivo design does not reduce the test takers' attentiveness. However, we observed that participants who interacted with the test interface reported a higher video QoE than other groups.}, language = {en} }