5-19 July, 2024, Niagra Falls, Canada

The first workshop on Surpassing Latency Limits in Adaptive Live Video Streaming (LIVES 2024) aims to bring together researchers and developers to satisfy the data-intensive processing requirements and QoE challenges of live video streaming applications through leveraging heuristic and learning-based approaches.

Delivering video content from a video server to viewers over the Internet is time-consuming in the streaming workflow and has to be handled to offer an uninterrupted streaming experience. The end-to-end latency, i.e., from the camera capture to the user device, particularly problematic for live streaming. Some streaming-based applications, such as virtual events, esports, online learning, gaming, webinars, and all-hands meetings, require low latency for their operation. Video streaming is ubiquitous in many applications, devices, and fields. Delivering high Quality-of-Experience (QoE) to the streaming viewers is crucial, while the requirement to process a large amount of data to satisfy such QoE cannot be handled with human-constrained possibilities. Satisfying the requirements of low latency video streaming applications require the streaming workflow to be optimized and streamlined all together, that includes: media provisioning (capturing, encoding, packaging, an ingesting to the origin server), media delivery (from the origin to the CDN and from the CDN to the end users), media playback (end user video player).

Please click here for more information.

On February 1st, 2024, Sahar Nasirihaghighi presented our work on ‘Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers’ at this year’s International Conference on Multimedia Modeling (MMM 2024) in Amsterdam, The Netherlands.

Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Heinrich Husslein, Klaus Schoeffmann

Abstract: Analyzing laparoscopic surgery videos presents a complex and multifaceted challenge, with applications including surgical training, intra-operative surgical complication prediction, and post-operative surgical assessment. Identifying crucial events within these videos is a significant prerequisite in a majority of these applications. In this paper, we introduce a comprehensive dataset tailored for relevant event recognition in laparoscopic gynecology videos. Our dataset includes annotations for critical events associated with major intra-operative challenges and post-operative complications. To validate the precision of our annotations, we assess event recognition performance using several CNN-RNN architectures. Furthermore, we introduce and evaluate a hybrid transformer architecture coupled with a customized training-inference framework to recognize four specific events in laparoscopic surgery videos. Leveraging the Transformer networks, our proposed architecture harnesses inter-frame dependencies to counteract the adverse effects of relevant content occlusion, motion blur, and surgical scene variation, thus significantly enhancing event recognition accuracy. Moreover, we present a frame sampling strategy designed to manage variations in surgical scenes and the surgeons’ skill level, resulting in event recognition with high temporal resolution. We empirically demonstrate the superiority of our proposed methodology in event recognition compared to conventional CNN-RNN architectures through a series of extensive experiments.


An EU funding programme enabling researchers to set up their own interdisciplinary research networks in Europe and beyond. #COSTactions

Representing Ireland with Prof. Horacio González-Vélez of National College of Ireland at the partner meeting of the Cost Action Cerciras – Connecting Education and Research Communities for an Innovative Resource Aware Society in Montpellier today.











Great alignment with several EU skills projects like ARISA – AI Skills, ESSA Software Skills Digital4Business and Digital4Security by facilitating transversal insights.


We’re seeking a passionate researcher for a PhD role in “Efficient Algorithms and Accelerator Architectures for Distributed Edge AI Systems”. This unique position offers the chance to work under the esteemed supervision of Prof. Radu Prodan (AAU Klagenfurt) and Prof. Marcel Baunach (TU Graz), with my guidance at SAL.


What You Will Do:
– Design & implement innovative distributed AI methods and algorithms.
– Customize these methods for the unique constraints of edge devices and networks.
– Investigate novel accelerator architectures for embedded AI applications.
– Explore quantization methods, with a focus on training and fine-tuning on edge devices.
– Publish research in high-impact journals and present at international conferences.

🎓 Candidate Profile:
– Master’s degree in a relevant field.
– Strong in programming and machine learning.
– Excellent communication skills in English.

🌍 Important Residency Note: Applicants should not have resided or carried out main activities in Austria for more than 12 months in the 3 years immediately before the application deadline.

Apply Now! Ensure to follow the specific application process outlined at Crystalline Program Recruitment (link is in the job description). https://lnkd.in/dBCY2xfe

ACM Mile High Video 2024 (mhv), Denver, Colorado, February 11-14, 2024

Authors: Daniele Lorenzi (Alpen-Adria-Universität Klagenfurt, Austria), Minh Nguyen (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: HTTP Adaptive Streaming (HAS) is the de-facto solution for delivering video content over the Internet. The climate crisis has highlighted the environmental impact of information and communication technologies (ICT) solutions and the need for green solutions to reduce ICT’s carbon footprint. As video streaming dominates Internet traffic, research in this direction is vital now more than ever. HAS relies on Adaptive BitRate (ABR) algorithms, which dynamically choose suitable video representations to accommodate device characteristics and network conditions. ABR algorithms typically prioritize video quality, ignoring the energy impact of their decisions. Consequently, they often select the video representation with the highest bitrate under good network conditions, thereby increasing energy consumption. This is problematic, especially for energy-limited devices, because it affects the device’s battery life and the user experience. To address the aforementioned issues, we propose E-WISH, a novel energy-aware ABR algorithm, which extends the already-existing WISH algorithm to consider energy consumption while selecting the quality for the next video segment. According to the experimental findings, E-WISH shows the ability to improve Quality of Experience (QoE) by up to 52% according to the ITU-T P.1203 model (mode 0) while simultaneously reducing energy consumption by up to 12% with respect to state-of-the-art approaches.

Keywords: HTTP adaptive streaming, Energy, Adaptive Bitrate (ABR), DASH


On Wednesday, December 20, 2023, Josef Hammer successfully defended his PhD thesis (“Transparent Access to 5G Edge Services”) under the supervision of  Univ.-Prof. DI Dr. Hermann Hellwagner and Univ.-Prof. DI Dr. Radu Prodan. The defense was chaired by Assoc.-Prof. DI Dr. Klaus Schöffmann and the examiners were Prof. Dr.-Ing. Amr Rizk (Universität Duisburg-Essen) and Univ.-Prof. Dipl.-Ing. Dr. Christian Timmerer. We are pleased to congratulate Dr. Josef Hammer on passing his Ph.D. exam!

In a noteworthy presence at the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023), Dragi Kimovski and Narges Mehran presented three workshop papers:

  1. Marcus Reitzl and Dragi Kimovski, “Multi-Objective Optimisation of Container Orchestration Systems”,
  2. Narges Mehran, Dragi Kimovski, Hermann Hellwagner, Dumitru Roman, Ahmet Soylu, and Radu Prodan, “Scheduling on the Computing Continuum: A Survey “,
  3. Narges Mehran, Arman Haghighi, Pedram Aminharati, Nikolay Nikolov, Ahmet Soylu, Dimitru Roman and Radu Prodan, “Comparison of Microservice Call Rate Prediction for Replication in the Cloud”. 

 Additionally, Dragi Kimovski took on the role of a session chair, leading discussions on the intricacies of scheduling in the computing continuum. 

Ran from December 15 to 17, 2023
Website: https://klujam.at/

With 165 registered participants and 35 games submitted, this was the largest game jam hosted in Klagenfurt to date!
A warm thank you to everyone who participated and/or helped – this event was a big success.

Same as every semester, students, teachers, alumni, and externals all worked together, formed teams, and made various games within 48 hours. We also had many international online participants joining us, e.g. from the Netherlands or even from Brazil.
This time, the topic was “Caution Fragile”, which was interpreted in different ways – from detective stories, board games and, pen-and-paper games, VR games, to classical arcade video games, the results were more varied than ever!

Please feel free to check out all the games here:

A final shoutout to Dynatrace, Plincs, Sensolligent, Fire Totem Games, Dirty Paws Studio, the FTF and the University for making this possible!

Our paper has been accepted at ICASSP 2024:

Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria), Mohammad Ghanbari (University of Essex, UK), and Christian Timmerer (AAU, Austria)

Abstract: With the ubiquity of video streaming, optimizing the delivery of video content while reducing energy consumption has become increasingly critical. Traditional adaptive streaming relies on a fixed set of bitrate-resolution pairs, known as bitrate ladders, for encoding. However, this one-size-fits-all approach is suboptimal for diverse video content. As a result, per-title encoding approaches dynamically select the bitrate ladder for each content. In this paper, we address the pressing issue of increasing energy consumption in video streaming by introducing GreenRes, a novel approach that goes beyond traditional quality-centric resolution selection. Instead, GreenRes considers both video quality and energy consumption to construct an optimal bitrate ladder tailored to the unique characteristics of each video content.
To achieve this, GreenRes, similar to per-title encoding, encodes each video content at various resolutions, each with a set of bitrates. It then establishes a maximum acceptable quality drop threshold and selects resolutions that not only maintain video quality above this threshold, but also minimize energy consumption. Our experimental results demonstrate a 30.82% reduction in energy consumption on average, while ensuring a maximum quality drop of 0.53 Video Multimethod Assessment Fusion (VMAF) points.