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

IEEE Senior Member is the highest professional grade for which an IEEE member can apply. This distinction requires extensive professional experience and demonstrated accomplishments that reflect technical expertise, leadership, and professional maturity. Fewer than 10% of IEEE’s nearly half a million members worldwide have achieved this honor.

Farzad’s elevation is a testament to his impactful research, dedication, and continued contributions to the field. We are delighted to celebrate this important milestone and look forward to his future achievements, innovative projects, and continued success as part of this distinguished community.

Please join us in congratulating Farzad on this outstanding accomplishment.

19 February 2026

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

ACM Transactions on Multimedia Computing, Communications and Applications

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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.

AI-Assisted Energy-Efficient Multimedia Systems

            The 17th ACM Multimedia System Conference (MMSys’26)

Hong Kong SAR

4th – 8th April 2026

Zoha Azimi

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.

Adaptive Compressed Domain Video Encryption

Expert Systems With Applications

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Mohammad Ghasempour (AAU, Austria), Yuan Yuan (Southwest Jiaotong University), Hadi Amirpour (AAU, Austria), Hongjie He (Southwest Jiaotong University), and Christian Timmerer (AAU, Austria)

Abstract: With the ever-increasing amount of digital video content, efficient encryption is crucial to protect visual content across diverse platforms. Existing methods often incur excessive bitrate overhead due to content variability. Furthermore, since most videos are already compressed, encryption in the compressed domain is essential to avoid processing overhead and re-compression quality loss. However, achieving both format compliance and compression efficiency while ensuring that the decoded content remains unrecognizable is challenging in the compressed domain, since only limited information is available without full decoding. This paper proposes an adaptive compressed domain video encryption (ACDC) method that dynamically adjusts the encryption strategy according to content characteristics. Two tunable parameters derived from the bitstream information enable adaptation to various application requirements. An adaptive syntax integrity method is employed to produce format-compliant bitstreams without full decoding. Experimental results show that ACDC reduces bitrate overhead by 48.2% and achieves a 31-fold speedup in encryption time compared to the latest state of the art, while producing visually unrecognizable outputs.

YTLive: A Dataset of Real-World YouTube Live Streaming Sessions

IEEE/IFIP Network Operations and Management Symposium (NOMS) 2026

 Rome, Italy- 18 – 22 May 2026

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Resource Management for Distributed Binary Neural Networks in Programmable Data Plane

IEEE/IFIP Network Operations and Management Symposium (NOMS) 2026

 Rome, Italy- 18 – 22 May 2026

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QoE Modeling in Volumetric Video Streaming: A Short Survey

IEEE/IFIP Network Operations and Management Symposium (NOMS) 2026

 Rome, Italy- 18 – 22 May 2026

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Volumetric video streaming enables six degrees of freedom (6DoF) interaction, allowing users to navigate freely within immersive 3D environments. Despite notable advancements, volumetric video remains an emerging field, presenting ongoing challenges and vast opportunities in content capture, compression, transmission, decompression, rendering, and display. As user expectations grow, delivering high Quality of Experience (QoE) in these systems becomes increasingly critical due to the complexity of volumetric content and the demands of interactive streaming. This paper reviews recent progress in QoE for volumetric streaming, beginning with an overview of QoE evaluation of volumetric video streaming studies, including subjective assessments tailored to 6DoF content. The core focus of this work is on objective QoE modeling, where we analyze existing models based on their input factors and methodological strategies. Finally, we discuss the key challenges and promising research directions for building perceptually accurate and adaptable QoE models that can support the future of immersive volumetric media.