Priv.-Doz. Farzad Tashtarian has been elevated to IEEE Senior Member in recognition of his contributions to multimedia streaming systems.
19 February 2026
ACM Transactions on Multimedia Computing, Communications and Applications
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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 title: AI-Assisted Energy-Efficient Multimedia Systems
Authors: 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.
Ttitle: Tell Your Story Through Games (TYS): Preliminary Guidelines for Mixed-Migrant Participatory Game Jams
Conference: The 3rd Geogames Symposium (3GGS) – Iowa, USA
Authors: Kseniia Harshina
Abstract: Migration-themed games are often framed as “empathy machines,” inviting outsiders to temporarily inhabit another’s hardship. This framing can slide into identity tourism and reinforce the trope of the “helpless refugee”. This short paper presents Tell Your Story Through Games (TYS) as a participatory game jam method that centers agency, and self-expression in mixed-migrant communities. We contribute preliminary methodological guidelines for running participatory story game jams with mixed-migrant communities. Participants with lived experience of migration and/or displacement create place-based story games, choosing how personal or fictional they want the story to be. One pilot iteration has been completed; future iterations will refine the method and evaluate its support for narrative agency and community-building.
Title: Pleasure Not For Everyone: Epistemic Injustice Towards Ukrainian Game Studies
Conference: 17th Annual DiGRA Conference
Authors: Kseniia Harshina, Mark Maletska
Abstract: Calls for playfulness in research often emphasize joy and learning, yet play and games can also exclude and harm minoritized participants—including within academia. In game studies, debates on diversity and postcoloniality have grown, but they still tend to center Western/Western European perspectives, which can obscure other forms of marginalization inside “Europe,” including Eastern Europe. This extended abstract summarizes an in-progress collaborative autoethnography by Ukrainian game studies scholars. Using Fricker’s concept of epistemic injustice, we argue that Ukrainian scholars face a dual barrier to participation in game studies: (1) structural inaccessibility within Ukraine (e.g., disrupted institutions, limited resources and mobility) and (2) epistemic misrecognition within Western institutions (e.g., being treated as subjects of humanitarian concern rather than credible theory producers). Together, these barriers shape who can enter the field and which knowledge is considered legitimate.
Adaptive Compressed Domain Video Encryption
Expert Systems With Applications
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.
Title: Indistinguishability Analysis of JPEG Image Encryption Schemes
Authors: Yuan Yuan, Lingfeng Qu, Ji Zhang, Ningxiong Mao, Hadi Amirpour
Abstract: JPEG images are widely used for communication and storage, making secure encryption essential for privacy protection. Existing JPEG encryption studies primarily rely on empirical metrics such as visual distortion, key space, or correlation, while overlooking the formal indistinguishability against chosen-plaintext attacks (IND-CPA) property. This work provides the first systematic analysis of existing JPEG encryption schemes from the IND-CPA perspective. A new metric, termed feature change rate, is introduced to quantify the preservation of residual features. Furthermore, the relationship between feature change rate and key estimation success under CPA is established, indicating that smaller feature changes result in higher attack accuracy. Based on these findings, we propose a set of design principles for constructing secure and practical JPEG encryption schemes. Finally, we outline a feature-changing encryption strategy that enhances IND-CPA security while maintaining JPEG compatibility and compression efficiency.
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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Mojtaba Mozhganfar (University of Tehran), Masoumeh Khodarahmi (IMDEA), Daniele Lorenzi (Bitmovin), Mahdi Dolati (Sharif University of Technology), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Ahmad Khonsari (University of Tehran), Christian Timmerer (Alpen-Adria-Universität Klagenfurt)
Abstract
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.
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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Fatemeh Babaei (Sharif University of Technology), Mahdi Dolati (Sharif University of Technology), Mojtaba Mozhganfar (University of Tehran), Sina Darabi (Università della Svizzera Italiana), Farzad Tashtarian (University of Klagenfurt)
Abstract
Programmable networks enable the deployment of customized network functions that can process traffic at line rate. The growing traffic volume and the increasing complexity of network management have motivated the use of data-driven and machine learning–based functions within the network. Recent studies demonstrate that machine learning models can be fully executed in the data plane to achieve low latency. However, the limited hardware resources of programmable switches pose a significant challenge for deploying such functions. This work investigates Binary Neural Networks (BNNs) as an effective mechanism for implementing network functions entirely in the data plane. We propose a network-wide resource allocation algorithm that exploits the inherent distributability of neural networks across multiple switches. The algorithm builds on the linear programming relaxation and randomized rounding framework to achieve efficient resource utilization. We implement our approach using Mininet and bmv2 software switches. Comprehensive evaluations on two public datasets show that our method attains near-optimal performance in small-scale networks and consistently outperforms baseline schemes in larger deployments.
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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Mojtaba Mozhganfar (University of Tehran), Pooya Jamshidi (University of Tehran), Seyyed Ali Aghamiri (University of Tehran), Mohsen Ghasemi (Sharif University of Technology), Mahdi Dolati (Sharif University of Technology), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Ahmad Khonsari (University of Tehran), Christian Timmerer (Alpen-Adria-Universität Klagenfurt)
Abstract
Live streaming plays a major role in today’s digital platforms, supporting entertainment, education, social media, etc. However, research in this field is limited by the lack of large, publicly available datasets that capture real-time viewer behavior at scale. To address this gap, we introduce YTLive, a public dataset focused on YouTube Live. Collected through the YouTube Researcher Program over May and June 2024, YTLive includes more than 507000 records from 12156 live streams, tracking concurrent viewer counts at five-minute intervals along with precise broadcast durations. We describe the dataset design and collection process and present an initial analysis of temporal viewing patterns. Results show that viewer counts are higher and more stable on weekends, especially during afternoon hours. Shorter streams attract larger and more consistent audiences, while longer streams tend to grow slowly and exhibit greater variability. These insights have direct implications for adaptive streaming, resource allocation, and Quality of Experience (QoE) modeling. YTLive offers a timely, open resource to support reproducible research and system-level innovation in live streaming. The dataset is publicly available at: https://github.com/ghalandar/YTLive.