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Workshop on Media Energy Consumption Measurement and Exposure

Sustainability in Video Encoding and Streaming:
Energy-Efficient Techniques and Metrics

Workshop on Media Energy Consumption Measurement and Exposure

[Workshop URL] [Slides] [PDF]

Presenter: Christian Timmerer (Alpen-Adria-Universität Klagenfurt

Abstract: The presentation discusses the increasing environmental impact of video streaming and highlights the urgent need for more sustainable approaches across the entire streaming pipeline. Video traffic dominates internet usage and contributes significantly to global greenhouse gas emissions, while the demand for higher quality content continues to drive up computational complexity and energy consumption in encoding, delivery, and playback.

A central insight is that there is a strong trade-off between video quality and energy consumption, where small reductions in quality can lead to substantial energy savings. By introducing energy as an explicit optimization objective, techniques such as content-aware encoding, energy-aware bitrate ladder construction, and real-time optimization for live streaming can significantly reduce energy usage while maintaining nearly the same perceptual quality.

The work also emphasizes the role of adaptive bitrate algorithms that incorporate energy consumption alongside traditional quality and buffer-based metrics. These approaches demonstrate that it is possible to simultaneously improve user experience and reduce energy consumption, indicating that sustainability and performance can be aligned rather than conflicting goals.

To enable such optimizations, the presentation introduces a range of metrics and models, including video complexity measures, quality prediction models, and machine learning-based approaches for estimating encoding and decoding energy as well as CO₂ emissions. These tools support more informed, data-driven decisions across the full streaming workflow from encoding to playback.

Another important theme is end-to-end optimization, where energy efficiency depends on the combined behavior of encoding strategies, bitrate selection, and client-side adaptation. Industry efforts confirm the practical relevance of these approaches and highlight the importance of collaboration and real-world validation.

Despite promising results, several challenges remain, including difficulties in measuring and benchmarking energy consumption, the lack of standardized methodologies, and the limited integration of energy considerations into existing workflows. Overall, the presentation argues that energy consumption should become a first-class optimization target in video streaming systems, similar to established quality metrics, to enable truly sustainable media delivery.

Keywords: sustainable streaming, energy-aware encoding, adaptive bitrate streaming, green multimedia, video compression, bitrate ladder optimization, QoE optimization, energy-quality tradeoff, video complexity analysis, CO2 footprint, energy modeling, machine learning for video, end-to-end optimization, eco-efficient streaming, real-time streaming optimization

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Paper accepted @ Text2Story 2026

Title: Dynamic Participatory Game Design with Local AI: From Interviews to Trauma-Aware Interactive Narratives

Authors: Kseniia Harshina, Tom Tucek, Mathias Lux

Location: TextStory 2026 – Delft, The Netherlands, March 2026

Abstract: We present a work-in-progress, trauma-aware participatory storytelling pipeline that uses a locally hosted large language model (LLM) as a neutral chatbot interviewer. The system supports self-paced narration without cloud processing, prioritizing privacy, data sovereignty, and participant control. Interview transcripts are transformed into a structured scene representation (extracted fields and dialogue prompts), which is then replayed through a lightweight prototype interface as an initial step toward interactive memory-based experiences. We report a small formative expert evaluation (n=2) focusing on perceived comfort, emotional safety, and usability. Participants described the interviewer as low-pressure and reflective, while highlighting limitations such as weak acknowledgement of long answers and occasional “forced turns.” We discuss design implications for narrative extraction, turn-taking, and staged evaluation in sensitive contexts, and outline next steps for community-informed studies with participants who have lived experience of displacement.

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Kurt Horvath has successfully defended his PhD thesis

On Thursday, February 26, 2026. Kurt successfully defended his PhD thesis (Service Discovery in the Computing Continuum) under the supervision of  Prof. Radu Prodan and Dr. Dragi Kimovski. The defense was chaired by Assoc.-Prof. DI Dr Klaus Schöffmann and the examiners were Prof. Valeria Cardellini (online) and Prof. Karin Anna Hummel (on-site). 

We are pleased to congratulate Dr. Kurt Horvath on successfully passing his Ph.D. examination!

University of Vienna Game Lab Visits Klagenfurt

On 27 February, Sabrina Größing and Dr Felix Schniz welcomed a delegation from the University of Vienna’s Game Lab to Klagenfurt. After exchanging origin stories, concepts and objectives, the warm‑hearted meeting quickly revealed shared ambitions and core values. The participants agreed to schedule follow‑up visits to both Vienna and Klagenfurt, deepening the partnership with the Klagenfurt Critical Game Lab and laying the groundwork for a burgeoning network of game labs across Austria.

 

Interview @ Deutschlandfunk Kultur

On 29 January, Dr Felix Schniz held an interview on the topic of Spirituality and Video Games for Deutschlandfunk Kultur. The interviews discussed his las publications and offered an insight into how technology, first and foremost video games, can help with the contemporary crisis of faith.

Paper accepted @ IEEE Access

Title: Lightweight WebAssembly-Based Intrusion Detection for Zero Trust Edge Networks

Authors: Jonathan Weber (TU Wien, Austria), Ilir Murturi (University of Prishtina, Kosova), Xhevahir Bajrami (University of Prishtina, Kosova), Reza Farahani (University of Klagenfurt, Austria), Praveen Kumar Donta (Stockholm University, Sweden), Schahram Dustdar (TU Wien, Austria)

Venue: IEEE Access

Abstract: IoT devices deployed across computing continuum infrastructures present significant security challenges due to resource constraints and decentralization. Traditional centralized intrusion detection systems struggle in such environments because of limited connectivity, high latency, and single points of failure. To address these challenges, this article extends a learning-driven Zero Trust framework tailored to resource-constrained edge environments and proposes an approach for evaluating lightweight intrusion detection models in such environments. Our extended approach enables systematic evaluation of lightweight machine learning models for localized intrusion detection, comprising three layers: (i) compilation, (ii) execution, and (iii) measurement. The proposed approach is implemented using Rust and WebAssembly to ensure portable, efficient, and isolated execution across heterogeneous devices. Using this framework, seven representative intrusion detection models (i.e., Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (KNN), Logistic Regression (LR), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) variants) were implemented and evaluated on the UNSW-NB15 dataset. Results show that RF achieved the best trade-off between detection accuracy and efficiency, while simpler models (DT and LR) offered near-instant inference with minimal resource usage, making them ideal for highly constrained devices. In contrast, more complex models such as deep neural networks and KNN introduced significant overhead for only modest accuracy gains. These findings underscore the need to balance accuracy and resource efficiency for effective Zero Trust edge security.

Paper accepted @ Internet Technology Letter

Title: Performance Evaluation of Privacy Models for Data Streams on the Edge

Authors: Ilir Murturi, Boris Sedlak, Reza Farahani, Schahram Dustdar

Venue: Internet Technology Letter

Abstract: Recent advances in edge computing enable data stream privacy enforcement directly on resource‐constrained devices, reducing latency and the exposure of sensitive information. In this paper, we extend and validate our previously proposed privacy‐enforcing framework, which allows high‐level privacy policies to be expressed as chains of triggers and transformations, executed at the edge. To assess its practical viability, we conduct a comprehensive performance profiling of multiple privacy models across heterogeneous edge hardware platforms. Six privacy‐model chains, ranging from basic face detection to combined face‐and‐person anonymization, are evaluated across three representative edge devices. Key performance metrics (i.e., execution time, CPU utilization, memory usage, and power consumption) are measured to inform optimal placement of privacy transformations. Our evaluation offers critical insights into the effectiveness of the privacy‐enforcing framework on resource‐constrained devices, thereby guiding practitioners in selecting suitable deployment targets for privacy‐preserving data stream analytics on the edge.

Farzad Tashtarian has been elevated to IEEE Senior Member

Priv.-Doz. Farzad Tashtarian has been elevated to IEEE Senior Member in recognition of his contributions to multimedia streaming systems.

19 February 2026

Vicor engineer Chris Swartz achieves senior member status at IEEE

Introduction to the Special Issue on ACM Multimedia Systems 2024 and Co-Located Workshops

ACM Transactions on Multimedia Computing, Communications and Applications

[PDF]

Authors: Christian Timmerer (AAU, Austria), Maria Martini (Kingston University London, UK), Ali C. Begen (Ozyegin University, Türkiye), Luca De Cicco (Politecnico di Bari, Italy)

Abstract: This special issue presents recent advances in multimedia systems research showcased at ACM Multimedia Systems 2024 and its co-located workshops. The selected papers span adaptive and immersive video streaming, low-latency and scalable delivery architectures, and innovations in video coding and processing. Together, they illustrate the rapid progress and broad impact of emerging techniques across the multimedia stack.

Paper accepted @ MMSys´26

Paper title: AI-Assisted Energy-Efficient Multimedia Systems

Authors: Zoha Azimi

Venue:  (Doctoral Symposium) MMSys’26, The 17th ACM Multimedia System Conference, Hong Kong SAR, 4th – 8th April 2026

Abstract: Video streaming constitutes the majority of today’s Internet traffic and is expected to continue growing in scale, complexity, and environmental impact. As video systems, from encoding to delivery, consume substantial computational resources, their energy footprint has emerged as a critical challenge for both industry and research communities. At the same time, recent advances in Artificial Intelligence (AI) and Generative AI have improved video quality and adaptive streaming performance, yet often at the cost of increased computational load and higher energy consumption. This increase creates a growing need for streaming systems that not only deliver high Quality of Experience (QoE) but also minimize energy usage across heterogeneous devices and network infrastructures. The scope of this doctoral study is within the end-to-end video ecosystem, focusing on balancing the trade-off between energy consumption and user experience. It aims to investigate and develop intelligent frameworks that treat sustainability as a primary metric alongside performance, improving energy efficiency across the video lifecycle without compromising the viewer’s experience. We present three fundamental research questions to target the related challenges in the domain of sustainable multimedia systems.