Christian Timmerer

Christian Timmerer has been appointed IEEE Communications Society Distinguished Lecturer for the term 2021-2022

Christian Timmerer, Associate Professor at the Institute of Information Technology (ITEC) and Director of the ATHENA Christian Doppler Laboratory, has been appointed IEEE Communications Society Distinguished Lecturer for the term 2021-2022.

“The Distinguished Lecturer Program (DLP) connects Senior IEEE ComSoc members, who are renowned communications technology experts, with ComSoc chapters so they can share their knowledge, expertise, and insights into the future of communications technology.”

In the context of the Distinguished Lecturer Program (DLP), Christian Timmerer will offer the following (virtual) lecture topics:

  • HTTP Adaptive Streaming (HAS) — Quo Vadis?
  • Quality of Experience (QoE) for Traditional and Immersive Media Services
  • Immersive Media Services: from Encoding to Consumption
  • 20 Years of Streaming in 20 Minutes
  • Multimedia Communication, Networking, Protocols, Delivery
  • Multimedia Standards (MPEG, IETF, W3C)

The details of how to request a Distinguished Lecturer can be found here.

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Bitmovin wins Technology & Engineering Emmy® Award

We are happy and proud to see Bitmovin among the 72nd Annual Technology & Engineering Emmy® Awards Recipients. The award is received for “Development of Massive Processing Optimized Compression Technologies” which acknowledges Bitmovin’s Encoding product including its reportedly best per-title encoding feature.

Bitmovins press release can be found here and approximately one year we had the official opening ceremony of the ATHENA project that will continue to fed the innovation pipeline with respect to HTTP Adaptive Streaming (HAS) and beyond. Please see our latest publications in this field and in case of questions please do not hesitate to contact us.

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OSCAR: On Optimizing Resource Utilization in Live Video Streaming

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Authors: Alireza Erfanian (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Anatoliy Zabrovskiy (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: Live video streaming traffic and related applications have experienced significant growth in recent years. However, this has been accompanied by some challenging issues, especially in terms of resource utilization. Although IP multicasting can be recognized as an efficient mechanism to cope with these challenges, it suffers from many problems. Applying software-defined networking (SDN) and network function virtualization (NFV) technologies enable researchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN concept, we introduce OSCAR (Optimizing reSourCe utilizAtion in live video stReaming) as a new cost-aware video streaming approach to provide advanced video coding (AVC)-based live streaming services in the network. In this paper, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder function (VTF). At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller.  Then, by executing a mixed-integer linear program (MILP), the SDN controller determines a group of optimal multicast trees for streaming the requested videos from an appropriate origin server to the VRPs. Moreover, to elevate the efficiency of resource allocation and meet the given end-to-end latency threshold, OSCAR delivers only the highest requested quality from the origin server to an optimal group of VTFs over a multicast tree. The selected VTFs then transcode the received video segments and transmit them to the requesting VRPs in a multicast fashion. To mitigate the time complexity of the proposed MILP model, we present a simple and efficient heuristic algorithm that determines a near-optimal solution in polynomial time. Using the MiniNet emulator, we evaluate the performance of OSCAR in various scenarios. The results show that OSCAR surpasses other SVC- and AVC-based multicast and unicast approaches in terms of cost and resource utilization.

Link: IEEE Transactions on Network and Service Management (TNSM)

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Live Video Streaming, Software Defined Networking (SDN), Video Transcoding, Network Function Virtualization (NFV).

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“Multidrone systems: More than the sum of the parts”, accepted for publication in IEEE Computer, 2021.

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Authors: Bernhard Rinner, Christian Bettstetter, Hermann Hellwagner, and Stephan Weiss

Abstract: Drones have evolved from bulky research platforms to everyday objects that enable a variety of innovative applications. One of the current challenges is to unite individual drones into an integrated autonomous system. They should operate as a networked team to provide novel functionality that multiple individual drones can never achieve. This article addresses the building blocks of such multidrone systems: wireless connectivity, communication, and coordination. We discuss implementation aspects in three experimental case studies, compare our techniques for improving resource efficiency, and present some “lessons learned” from our research experience in this area.

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Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate Algorithms

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NOSSDAV’21: The 31st edition of the Workshop on Network and Operating System Support for Digital Audio and Video
Sept. 28-Oct. 1, 2021, Istanbul, Turkey
Conference Website

Authors: Babak Taraghi (Alpen-Adria-Universität Klagenfurt), Abdelhak Bentaleb (National University of Singapore), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), Roger Zimmermann (National University of Singapore) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: Adaptive BitRate (ABR) algorithms play a crucial role in delivering the highest possible viewer’s Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS – the dominant video streaming technique on the Internet – to deliver the best QoE for their users. Viewer’s delightfulness relies heavily on how the ABR of a media player can adapt the stream’s quality to the current network conditions. QoE for end-to-end video streaming sessions has been evaluated in many research projects to give better insight into the quality metrics. Objective evaluation models such as ITU Telecommunication Standardization Sector (ITU-T) P.1203 allow for the calculation of Mean Opinion Score (MOS) by considering various QoE metrics, and subjective evaluation is the best assessment approach in investigating the end-user opinion over a video streaming session’s experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper’s main contribution is subjective evaluation analogy with objective evaluation for well-known heuristic-based ABRs.

Keywords: HTTP Adaptive Streaming, ABR Algorithms, Quality of Experience, Crowdsourcing, Subjective Evaluation, Objective Evaluation, MOS, (ITU-T) P.1203

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Paper accepted at the 21st IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021)

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The paper “A Two-Sided Matching Model for Data Stream Processing in the Cloud–Fog Continuum” has been accepted for publication at the 21st IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021).

Authors: Narges Mehran, Dragi Kimovski, and Radu Prodan

Abstract: Latency-sensitive and bandwidth-intensive stream processing applications are dominant traffic generators over the Internet network. A stream consists of a continuous sequence of data elements, which require processing in nearly real-time. To improve communication latency and reduce the network congestion, Fog computing complements the Cloud services by moving the computation towards the edge of the network. Unfortunately, the heterogeneity of the new Cloud–Fog continuum raises important challenges related to deploying and executing data stream applications. We explore in this work a two-sided stable matching model called Cloud–Fog to data stream application matching (CODA) for deploying a distributed application represented as a workflow of stream processing microservices on heterogeneous Cloud–Fog computing resources. In CODA, the application microservices rank the continuum resources based on their microservice stream processing time, while resources rank the stream processing microservices based on their residual bandwidth. A stable many-to-one matching algorithm assigns microservices to resources based on their mutual preferences, aiming to optimize the complete stream processing time on the application side, and the total streaming traffic on the resource side.
We evaluate the CODA algorithm using simulated and real-world Cloud–Fog scenarios. We achieved 11 to 45 % lower stream processing time and 1.3 to 20 % lower streaming traffic compared to related state-of-the-art approaches.

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Computer Graphics Course in SS 2021 – Prof. Carsten Griwodz

ITEC is delighted to announce the next speaker in our guest lecture series – Prof. Carsten Griwodz from the University of Oslo & SIMULA Research Laboratory, Norway. The online-course will take place from March 3 – May 28, 2021.

This course is meant to provide the participants with the means for evaluating end-user satisfaction with interactive applications. Please register at the course 780.411.

Further information is available HERE.

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Paper accepted at XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection using convolutional Neural Networks

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Authors: Vishu Madaan (Lovely Professional University, India), Aditya Roy (Lovely Professional University, India), Charu Gupta (Bhagwan Parashuram Institute of Technology, New Delhi, India), Prateek Agrawal (Institute of ITEC, University of Klagenfurt, Austria), Cristian Bologa (Babes-Bolyai University, Cluj-Napoca, Romania) and Radu Prodan (Institute of ITEC, University of Klagenfurt, Austria).

Abstract: COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RTPCR) tests which may take longer than 48 hours. This is is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

Journal: New Generation Computing

Acknowledgement: This work is partially supported by ARTICONF

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DCC’21: SLFC: Scalable Light Field Coding

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Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), and Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK)

Abstract: Light field imaging enables some post-processing capabilities like refocusing, changing view perspective, and depth estimation. As light field images are represented by multiple views they contain a huge amount of data that makes compression inevitable. Although there are some proposals to efficiently compress light field images, their main focus is on encoding efficiency. However, some important functionalities such as viewpoint and quality scalabilities, random access, and uniform quality distribution have not been addressed adequately. In this paper, an efficient light field image compression method based on a deep neural network is proposed, which classifies multiple views into various layers. In each layer, the target view is synthesized from the available views of previously encoded/decoded layers using a deep neural network. This synthesized view is then used as a virtual reference for the target view inter-coding. In this way, random access to an arbitrary view is provided. Moreover, uniform quality distribution among multiple views is addressed. In higher bitrates where random access to an arbitrary view is more crucial, the required bitrate to access the requested view is minimized.

Keywords: Light field, Compression, Scalable, Random Access.

Data Compression Conference (DCC)

23-26 March 2021, Snowbird, Utah, USA

https://www.cs.brandeis.edu/~dcc

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