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Schwind, A., Moldovan, C., Janiak, T., Dworschak, N.-D., Hoßfeld, T.: Don’t Stop the Music: Crowdsourced QoE Assessment of Music Streaming with Stalling. 12th International Conference on Quality of Multimedia Experience (QoMEX). , Athlone, Ireland (2020).
Streaming made a lasting effect on the way our society consumes media in the last decade. While due to streaming the way we listen to music and podcasts has changed drastically, there are very few studies about its Quality of Experience (QoE) and possible influence factors. From video QoE studies, we know that, for example, undesirable stops of the stream (stalling events) have a significant impact on QoE. However, the way in which music and video streaming is consumed differs significantly, as music is often played in the background, and thus, the influence of stalling could be significantly different. Thus, this work evaluates the impact of stalling on music streaming QoE. Therefore, we conduct two crowdsourced user studies: In the first study, users have to rate four songs with different stalling patterns and evaluate the degree of impairments. Afterwards, we compare the ratings to the results of a lab study and show that they are highly correlated, and that crowdsourcing is a suitable way of measuring music streaming QoE. In addition, we conduct a second crowdsourcing study to investigate the influence of the user's attentiveness on QoE. Here, participants have to listen to one song with two stalling events, while one half of them had to transcribe a handwritten text with music playing in the background. The attentiveness shows no influence on the perceived streaming quality, but it shows a significant influence on the perceived quality degradation due to stalling events. Furthermore, considerably more stalling events were missed for workers who focused on the transcription. These results are an important step towards establishing new methods for investigating QoE in multimedia.
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Moldovan, C., Loh, F., Seufert, M., Hoßfeld, T.: Optimizing HAS for 360-Degree Videos. 5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet). , Budapest, Hungary (2020).
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Loh, F., Wamser, F., Moldovan, C., Zeidler, B., Tsilimantos, D., Valentin, S., Hoßfeld, T.: Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic. 32th IEEE/IFIP Network Operations and Management Symposium. , Budapest, Hungary (2020).
Today’s traffic projections speak of almost 58 % video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50 % encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.
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Moldovan, C.: User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements, (2019).
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Loh, F., Wamser, F., Moldovan, C., Zeidler, B., Hoßfeld, T., Tsilimantos, D., Valentin, S.: From click to playback: a dataset to study the response time of mobile YouTube. Proceedings of the 10th ACM Multimedia Systems Conference. pp. 267-272 (2019).
Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available.
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Moldovan, C.: Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks. , 2019 International Conference on Networked Systems (NetSys), Garching, Germany (2019).
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Moldovan, C.: Contributions to TF "Managing Web & Cloud QoE". , 13th Qualinet General Meeting, Berlin, Germany (2019).
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Schwind, A., Janiak, L., Moldovan, C., Wamser, F., Hoßfeld, T.: Peeking under the Hood – How the Measurement Setup Influences the Video Streaming Behavior. The 3rd International Workshop on Quality of Experience Management (QoE-Management 2019). , Paris, France (2019).
Global Internet video traffic will dramatically increase in the next years. With this rapid growth, interest in the application behavior of video streaming services and the resulting user experience rises. In particular, there is a need to understand the influence of system parameters on the streaming performance. Thus, several monitoring approaches have been developed which allow conducting automated measurements on a large-scale, for example, lightweight approaches for measurements running in the mobile networks or setups using a virtual frame buffer for servers without a display running in virtualized cloud environments. In some cases, the measurement hardware can totally be controlled, in other cases, there is no knowledge about parallel running services. In this paper, we answer the question whether and how much the measurement setup (e.g., virtualization, headless browsers, load on machines) influences the video streaming behavior. Therefore, we compare eight management setups with the ground truth (an end user watching a video on the own device) by evaluating the key performance indicators on application layer during video streaming, i.e. initial video playout delays and stalling. Our results reveal important insights: some of the common measurement setups heavily influence the measurements and must be avoided to collect reliable results.
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Moldovan, C., Wamser, F., Hoßfeld, T.: Energy-Efficient Adaptation Logic for HTTP Streaming in Mobile Networks. 2019 International Conference on Networked Systems (NetSys) (2019).
The requirements for video streaming have changed drastically during the past years. In today's Internet, high definition resolutions are considered default for videos, even in mobile settings, and with 4G penetration reaching 90 percent in the US, this no longer poses a big problem. However, while mobile bandwidth has increased, the battery life time of mobile devices has not increased significantly. Furthermore, current data plans are still not large enough to regularly stream movies during the commute. Users still resort to downloading media before travel. In this paper we propose a new HTTP adaptive streaming algorithm that delivers videos in high quality while avoiding stalling events, schedules the download of video segments so that a energy conserving idle state is often reached and keeps the buffer low at points in the video where many viewers abandon the video to save data. While most adaptive streaming algorithms optimize quality and stalling, this is the first attempt to use an adaptive streaming algorithm to reduce energy consumption. Since video streaming providers mostly care about the Quality of Experience when watching videos, energy efficiency is left to the device manufacturers. Therefore, both parties have little incentive to cooperate in this regard. But on the Internet of tomorrow, where most videos are watched on mobile devices, energy efficiency and the Quality of Experience must go hand in hand.
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Moldovan, C., Wamser, F., Hoßfeld, T.: User Behavior and Engagement of a Mobile Video Streaming User from Crowdsourced Measurements. 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) (2019).
Mobile video streaming has gained a lot of popularity in recent year with the introduction of large data plans for mobile phones. While users in non-mobile scenarios have become accustomed to high quality and few stalling events, this is not the case in mobile environments. People are more likely to tolerate stalling since they know high throughput coverage cannot always be guaranteed. Their behavior and their engagement are drastically different when watching videos on mobile devices. In this paper, we characterize mobile phone users who use a video streaming application. We present a data set of over 6,000 video views from a crowdsourced video measurement study. We investigate user activity and engagement during video streaming and how such metrics are correlated with each other. This is the first study that goes beyond user engagement and investigates the direct behavior of mobile video users outside the lab which is an important step towards mobile QoE management.
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Moldovan, C.: Viability of Wi-Fi Caches in an Era of HTTPS Prevalence. , 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France (2017).
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Surminski, S., Moldovan, C., Hoßfeld, T.: Saving Bandwidth by Limiting the Buffer Size in HTTP Adaptive Streaming. MMBnet 2017 Proceedings of the 9th GI/ITG Workshop. , Hamburg, Germany (2017).
Video streaming is one of the most bandwidth-intensive applications on the Internet. In HTTP adaptive video streaming the video quality is selected according to the available bandwidth. To compensate bandwidth fluctuations, players use a buffer in order to ensure a smooth video output. On one hand, if the buffer runs empty, the video playback stops, which users experience as negative. On the other hand, if the user aborts video playback, the video in the buffer was unnecessarily transmitted, hence this bandwidth was wasted. In this paper, we present a study in which we investigate the behavior of two video players and different buffer configurations in real-world bandwidth scenarios. Thereby, we focus on the dimensioning of the buffer size and the tradeoff between wasted bandwidth and the playback quality
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Moldovan, C.: Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS, (2017).
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Moldovan, C.: YouTube Can Do Better: Getting the Most Out of Video Adaptation. , ITC and IEEE Workshop on QoE Centric Management (QCMan), Würzburg, Germany (2016).
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Moldovan, C.: Impact of Variances on the QoE in Video Streaming. , ITC and IEEE Workshop on QoE Centric Management (QCMan), Würzburg, Germany (2016).
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Moldovan, C.: Bridging the Gap between QoE and User Engagement in HTTP Video Streaming. , 28th International Teletraffic Congress (ITC 28), Würzburg, Germany (2016).
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Moldovan, C.: QoE in Video Streaming - Example: Context Awareness. , ITG 5.2.1 Workshop Performance Evaluation and Optimisation of Communication Networks, Hamburg, Germany (2016).
