Christian Moldovan

Academic Staff

Christian Moldovan, M.Sc.

SGW 107 (Paluno)
+49 201 18-33179
Consultation Hour:
Termin auf Anfrage per Email

Curriculum Vitae:

Christian Moldovan received his Master's degree in Computer Science in 2014 from the University of Würzburg. Currently, he is pursuing his Ph.D as a researcher at the University of Duisburg-Essen. His research focuses on performance modeling of mechanisms for multimedia applications in the Internet.


  • Moldovan, Christian; Skorin-Kapov, Lea; Heegaard, Poul; Hoßfeld, Tobias: Optimal Fairness and Quality in Video Streaming With Multiple Users. In: Ieee (Ed.): 30th International Teletraffic Congress (ITC 30). Vienna, Austria 2018. CitationDetails
  • Sebastian Surminski, Christian Moldovan; Hoßfeld, Tobias: Practical QoE Evaluation of Adaptive Video Streaming. In: Reinhard German, Kai-Steffen Hielscher; Krieger, Udo R. (Ed.): Measurement, Modelling and Evaluation of Computing Systems. Springer International Publishing, Cham 2018, p. 283-292. Full textCitationDetails
  • Seufert, Michael; Moldovan, Christian; Burger, Valentin; Hoßfeld, Tobias: Applicability and Limitations of a Simple WiFi Hotspot Model for Cities. In: 13th International Conference on Network and Service Management (CNSM). Tokyo, Japan 2017. CitationDetails
  • Moldovan, Christian; Hoßfeld, Tobias; Hagn, Korbinian; Sieber, Christian; Kellere, Wolfgang: Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS. In: 29th International Teletraffic Congress (ITC 29). Genoa, Italy 2017. Full textCitationDetails
  • Sebastian Surminski, Christian Moldovan; Hoßfeld, Tobias: Saving Bandwidth by Limiting the Buffer Size in HTTP Adaptive Streaming. In: Krieger, Udo R.; Schmidt, Thomas C.; Timm-Giel, Andreas (Ed.): MMBnet 2017 - Proceedings of the 9th GI/ITG Workshop „Leistungs-, Verlässlichkeits- und Zuverlässigkeitsbewertung von Kommunikationsnetzen und Verteilten Systemen“. University of Bamberg Press, Hamburg 2017, p. 5-21. doi:10.20378/irbo-49762Full textCitationDetails
  • Moldovan, Christian; Metzger, Florian; Surminski, Sebastian; Hoßfeld, Tobias; Burger, Valentin: Viability of Wi-Fi Caches in an Era of HTTPS Prevalence. In: Society, Ieee Communications; Electrical, Institute Of; Electronics Engineers, Institute of Electrical; Engineers, Electronics (Ed.): IEEE ICC'17: Bridging People, Communities, and Cultures. Paris, France 2017. Full textCitationDetails
  • Moldovan, Christian; Metzger, Florian: Bridging the Gap Between QoE and User Engagement in HTTP Video Streaming. In: 28th International Teletraffic Congress (ITC 28). Würzburg, Germany 2016. Full textCitationDetails
  • Moldovan, Christian; Sieber, Christian; Heegaard, Poul; Kellerer, Wolfgang; Hoßfeld, Tobias: YouTube Can Do Better: Getting the Most Out of Video Adaptation. In: QCMAN 2016 : Fourth IEEE International Workshop on Quality of Experience Centric Management. Würzburg 2016. Full textCitationDetails
  • Moldovan, Christian; Hoßfeld, Tobias: Impact of Variances on the QoE in Video Streaming. In: QCMAN 2016 : Fourth IEEE International Workshop on Quality of Experience Centric Management. Würzburg 2016. Full textCitationDetails
  • Metzger, Florian; Lioutou, Eirini; Moldovan, Christian; Hoßfeld, Tobias: TCP Video Streaming and Mobile Networks: Not A Love Story, But Better With Context. In: Special Issue of Computer Networks on “Traffic and Performance in the Big Data Era”, Vol 2016 (2016). Full textCitationDetails
  • Wamser, Florian; Casas, Pedro; Seufert, Michael; Moldovan, Christian; Tran-Gia, Phuoc; Hoßfeld, Tobias: Modeling the YouTube Stack: from Packets to Quality of Experience. In: Computer Networks (2016). doi:10.1016/j.comnet.2016.03.020Full textCitationDetails
    YouTube is one of the most popular and volume-dominant services in today’s Internet, and has changed the Web for ever. Consequently, network operators are forced to consider it in the design, deployment, and optimization of their networks. Taming YouTube requires a good understanding of the complete YouTube stack, from the network streaming service to the application itself. Understanding the interplays between individual YouTube functionalities and their implications for traffic and user Quality of Experience (QoE) becomes paramount nowadays. In this paper we characterize and model the YouTube stack at different layers, going from the generated network traffic to the QoE perceived by the users watching YouTube videos. Firstly, we present a network traffic model for the YouTube flow control mechanism, which permits to understand how YouTube provisions video traffic flows to users. Secondly, we investigate how traffic is consumed at the client side, deriving a simple model for the YouTube application. Thirdly, we analyze the implications for the end user, and present a model for the quality as perceived by them. This model is finally integrated into a system for real time QoE-based YouTube monitoring, highly useful to operators to assess the performance of their networks for provisioning YouTube videos. The central parameter for all the presented models is the buffer level at the YouTube application layer. This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they are doing in practice in delivering YouTube videos to their customers.
  • Liotou, Eirini; Hoßfeld, Tobias; Moldovan, Christian; Metzger, Florian; Tsolkas, Dimitris; Passas, Nikos: Enriching HTTP Adaptive Streaming with Context Awareness - A Tunnel Case Study. In: CQRM - Communications QoS, Reliability and Modeling Symposium. ICC, 2016. Full textCitationDetails
  • Seufert, Michael; Burger, Valentin; Wamser, Florian; Tran-Gia, Phuoc; Moldovan, Christian; Hoßfeld, Tobias: Utilizing Home Router Caches to Augment CDNs toward Information-Centric Networking. In: EuCNC 2015. Paris, France 2015. Full textCitationDetails
  • Hoßfeld, Tobias; Moldovan, Christian; Schwartz, Christian: To Each According to his Needs: Dimensioning Video Buffer for Specific User Profiles and Behavior. In: QCMAN 2015. Ottawa, Canada 2015. PDFCitationDetails


  • Neue Internetanwendungen (Übung, WS 14/15)
  • Diskrete Simulation (Vorlesung und Übung, SS 15, SS 18)
  • Modelle der Informatik (Übung, WS 15/16, WS 17/18)
  • Concurrency (Übung, SS 16)
  • Master-Projektgruppe BeDeLeVe (SS 17)

Tutored Theses:

  • Using Viewing Statistics to Re- duce Wasted Traffic and En- ergy Consumption in HTTP Adap- tive Streaming (Master Thesis Computer Science, 2018) Details

    The energy consumption of mobile devices is a growing concern. Smartphones improve
    their capabilities faster than their batteries. Resource hungry applications like streaming
    services consume a lot of energy. The wireless interface, which uses a lot of energy, is
    empirically configured to provide a good performance for most applications. Adaptive
    video streaming, which is a popular streaming application, inefficiently utilises the wireless
    interface and causes traffic overhead. We develop an adaptation strategy, which utilises
    viewing statistics to reduce the energy consumption and traffic overhead. Additionally, the
    adaptation strategy optimises the QoS, to improve the users video streaming experience.
    We test and evaluate our strategy, using a simulation, which simulates users in a mobility
    scenario. We simulate users, who are watching multiple videos and can skip and abandon
    theplayback. Weshowthatenergyandtrafficcanbesaved, withmostlynegligibleinfluence
    on the QoS. Energy efficiency and traffic saving mostly describe a tradeoff. Different
    contexts offer different opportunities to save either more energy or more traffic. Further,
    we show, that throughput fluctuations are an important influence factor for our adaptation

  • Design and Simulative Performance Evaluation of a QoE Fair Adaptive Streaming Mechanism (Master Thesis Computer Science, 2017) Details

    In this thesis, we design a Quality of Experience (QoE) fair adaptive streaming algorithm and a simulation to evaluate its performance. We propose a design with an external coordinator that assesses QoE for clients behind a bottleneck and tries to maximize the QoE while reducing stalling and low video quality playback. The simulation was written with simpy, a discrete event framework in Python. We simulated multiple clients with configured and random arrival times and assessed the performance of the design adaptation strategy in comparison to other throughput- respectively buffer-based adaptation strategies. We found that the adaptation strategy did not outperform the buffer-based strategy on fairness and quality level playback

  • Implementation of a Browser-Plugin for QoS and QoE Monitoring of WebRTC-Based Video Conferencing (Bachelor Thesis Computer Science, 2017) Details

    In this paper, Web Real-Time Communication (WebRTC) monitoring tool will be implemented and by using this tool, it is possible to analyse Quality of Experience (QoE) of WebRTC video conferencing. Furthermore based on research of test result, a relationship between network layer parameters and application layer parameters will be find. This monitoring tool works on web browser of google Chrome, with ability to show up real time informations and it also allow users to storage their test data at least in local disk.

  • The Influence of the Buffer Size on the QoS in HTTP Adaptive Streaming (Master Thesis Computer Science, 2017) Details

    Video streaming has become one of the most bandwidth-consuming services on the Internet. As in video streaming the video is played while being downloaded, this process is sensitive to variations in the available bandwidth. Especially outages during the transmission can lead to quality degradations or even so called stalling events, in case the playback stops, because the player ran out of data. Stalling has a massive influence on the Quality of Experience (QoE). Variations in the bandwidth can be compensated with buffering. But with a larger buffer, not only the initial waiting time, which denotes the time until a video starts playing, but also the overall bandwidth consumption rises, because a large quantity of the videos are aborted before they end. In numbers, about 40% of all viewed videos on YouTube were aborted within the first 30 seconds, 20% of the videos were played less than ten seconds [Fin+11]. When a video is aborted, all video in the buffer is lost and can be counted as ‘wasted’. This generates significant costs for both user and service-provider. Transmitting data costs money for the provider of the video service, for server capacity as well as for traffic. But the transmission can also induce costs for the user. On mobile plans, it is often paid for a fixed amount of traffic. More traffic equals higher prices. Additionally, transmitting data on mobile devices consumes energy and therefore reduces battery lifetime. Summing up, a compromise between buffering and user experience has to be found. In this work, we investigate two different video players and their behavior under realistic scenarios while being on the move, in typical commuting situations. Hereby, we focus on the effect of buffering on the playback. As the user experience is still a heavily discussed field, we obtain objective Quality of Service key numbers of the playback. These are then be used to derive QoE numbers using different methods.