Mobile Instant Messaging (MIM) applications such as WhatsApp transformed human communication by enabling global exchange of various message types, such as text, image, video, or voice, at any time. Network providers are facing a substantial user base and network load which is especially high in group chats where each message needs to be distributed to each member. Due to end-to-end encryption, network operators must obtain knowledge about the communication and the resulting load on the network by other means, which makes it necessary to model the network traffic of MIM. In this work, we therefore present an approach to source traffic modeling for MIM. After identifying the building blocks of a Source Traffic Model (STM) for MIM, we address existing gaps through studies on MIM communication networks, contact proximity, media compression and payload size, as well as media file size distribution. Combining existing literature and our work, we present and implement a modular STM approach which can be used for developing STMs for MIM. Using an exemplary STM, we evaluate the daily network traffic per user. With this, we provide a comprehensive description of MIM in the network researching context and enable consideration of MIM in future network design.

Video conferencing has become indispensable in both professional and social contexts. From a networking point of view, timely distribution of media to all clients participating in a video conference is required. However, fluctuating network conditions pose a challenge, so that a high Quality of Experience (QoE) might not always be achieved. When assessing how network conditions impact the QoE of video conferencing, previous QoE studies focused on one-to-one settings and suffered from small numbers of participants due to their costly laboratory setups. In this work, we investigate crowdsourcing-based QoE studies for video conferencing in order to obtain a higher number and more globally distributed and diverse participants. For this, we consider multi-party video conferencing and design a web-based study framework which provides a high realism to online study participants by involving themselves in the presented video call. Finally, we conduct a QoE study with our framework to demonstrate the suitability of crowdsourcing for assessing the QoE impact of network-level and application-level degradations of video conferencing applications.

This paper explores the relationship between loading times and Quality of Experience (QoE) in Mobile Instant Messaging (MIM) applications. Using a web application that mimics MIM interfaces, we conducted a QoE study in which participants engaged with a virtual chat partner. We controlled image loading times during chatting and evaluated their impact on QoE, annoyance, and acceptance ratings. Although the results show no difference in the QoE ratings, they clearly show that longer delays lead to greater annoyance and lower user acceptance. These findings underscore the importance for MIM app providers to minimize loading times in order to increase user satisfaction and retention.

Reaching close-to-optimal bandwidth utilization in dynamic adaptive streaming over HTTP (DASH) systems can, in theory, be achieved with a small discrete set of bit rate representations. This includes typical bit rate ladders used in state-of-the-art DASH systems. In practice, however, we demonstrate that bandwidth utilization, and consequently the quality of experience (QoE), can be improved by offering a continuous set of bit rate representations, i.e., a continuous bit rate slide (COBIRAS). Moreover, we find that the buffer fill behavior of different standard adaptive bit rate (ABR) algorithms is sub-optimal in terms of bandwidth utilization. To overcome this issue, we leverage COBIRAS’ flexibility to request segments with any arbitrary bit rate and propose a novel ABR algorithm MinOff , which helps maximizing bandwidth utilization by minimizing download off-phases during streaming. To avoid extensive storage requirements with COBIRAS and to demonstrate the feasibility of our approach, we design and implement a proof-of-concept DASH system for video streaming that relies on just-in-time encoding ( JITE ), which reduces storage consumption on the DASH server. Finally, we conduct a performance evaluation on our testbed and compare a state-of-the-art DASH system with few bit rate representations and our JITE DASH system, which can offer a COBIRAS, in terms of bandwidth utilization and video QoE for different ABR algorithms.

Streaming has become the dominant source of media consumption, which not only applies to the widely researched field of video streaming, but also to music streaming. Here, previous studies so far have only researched the impact of streaming aspects, such as stalling events or initial loading times, on the QoE of music streaming. However, when using a music streaming application, users are already facing waiting times along the click path before they can start the actual streaming. These waiting times are caused by browsing delays, e.g., during searching for songs or scrolling through playlists, and can potentially deteriorate the QoE of the music streaming application. In this work, we conduct an online QoE study to quantify the impact of these browsing delays with the support of an emulated mobile music streaming web app. We found that browsing delays have no impact on the music streaming QoE, which shows that users are able to clearly distinguish between the two main functionalities of such apps, namely, browsing and streaming. However, browsing delays significantly reduce the QoE of the entire music streaming application, to a similar extent as if QoE degradations happen during the actual streaming. This shows that both browsing and streaming are equally important and have to be considered when designing music streaming annlications.