Hadi
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Accepted tutorial: Video Coding Advancements in HTTP Adaptive Streaming

Tutorial title: Video Coding Advancements in HTTP Adaptive Streaming

Venue: IEEE International Conference on Multimedia & Expo (ICME) 2025 (https://2025.ieeeicme.org/tutorials/)

We are happy to announce that our tutorial “Video Coding Advancements in HTTP Adaptive Streaming” (by Hadi Amirpourazarian and Christian Timmerer) has been accepted for IEEE ICME 2025, which will take place in Nantes, France, June 30 – July 4, 2025.

Description: This tutorial provides a comprehensive exploration of the HTTP Adaptive Streaming (HAS) pipeline, covering advancements from content provisioning to content consumption. We begin by tracing the history of video streaming and the evolution of video coding technologies. Attendees will gain insights into the timeline of significant developments, from early proprietary solutions to modern adaptive streaming standards like HAS. A comparative analysis of video codecs is presented, highlighting milestones such as H.264, HEVC, and the latest standard, Versatile Video Coding (VVC), emphasizing their efficiency, adoption, and impact on streaming technologies. Additionally, new trends in video coding, including AI-based coding solutions, will be covered, showcasing their potential to transform video compression and streaming workflows.

Building on this foundation, we explore per-title encoding techniques, which dynamically tailor bitrate ladders to the specific characteristics of video content. These methods account for factors such as spatial resolution, frame rate, device compatibility, and energy efficiency, optimizing both Quality of Experience (QoE) and environmental sustainability. Next, we highlight cutting-edge advancements in live streaming, including novel approaches to optimizing bitrate ladders without introducing latency. Fast multi-rate encoding methods are also presented, showcasing how they significantly reduce encoding times and computational costs, effectively addressing scalability challenges for streaming providers.

The tutorial further delves into edge computing capabilities for video transcoding, emphasizing how edge-based architectures can streamline the processing and delivery of streaming content. These approaches reduce latency and enable efficient resource utilization, particularly in live and interactive streaming scenarios.

Finally, we discuss the QoE parameters that influence both streaming and coding pipelines, providing a holistic view of how QoE considerations guide decisions in codec selection, bitrate optimization, and delivery strategies. By combining historical context, theoretical foundations, and practical insights, this tutorial equips attendees with the knowledge to navigate and address the evolving challenges in video streaming applications.

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Papers accepted: Efficient Location-Based Service Discovery for IoT and Edge Computing in the 6G Era & Enhancing Traffic Safety with AI and 6G: Latency Requirements and Real-Time Threat Detection

Efficient Location-Based Service Discovery for IoT and Edge Computing in the 6G Era

Authors: Kurt Horvath, Dragi Kimovski

Conference: 2025 10th International Conference on Information and Network Technologies (ICINT 2025)

Abstract: Efficient service discovery is a cornerstone of the rapidly expanding Internet of Things (IoT) and edge computing ecosystems, where low latency and localized service provisioning are critical. This paper proposes a novel location-based DNS (Domain Name System) method that leverages Location Resource Records (LOC RRs) to enhance service discovery. By embedding geographic data in DNS responses, the system dynamically allocates services to edge nodes based on user proximity, ensuring reduced latency and improved Quality of Service (QoS). Comprehensive evaluations demonstrate minimal computational overhead, with processing times below 1 ms, making the approach highly suitable for latency-sensitive applications. Furthermore, the proposed methodology aligns with emerging 6G standards, which promise sub-millisecond latency and robust connectivity. Future research will focus on real-world deployment, validating the approach in dynamic IoT environments. This work establishes a scalable, efficient, and practical framework for location aware service discovery, providing a strong foundation for next generation IoT and edge-computing solutions.

 

Enhancing Traffic Safety with AI and 6G: Latency Requirements and Real-Time Threat Detection

Authors: Kurt Horvath, Dragi Kimovski, Stojan Kitanov, Radu Prodan

Conference: 2025 10th International Conference on Information and Network Technologies (ICINT 2025)

Abstract: The rapid digitalization of urban infrastructure opens the path to smart cities, where IoT-enabled infrastructure enhances public safety and efficiency. This paper presents a 6G and AI-enabled framework for traffic safety enhancement, focusing on real-time detection and classification of emergency vehicles and leveraging 6G as the latest global communication standard. The system integrates sensor data acquisition, convolutional neural network-based threat detection, and user alert dissemination through various software modules of the use case. We define the latency requirements for such a system, segmenting the end-toend latency into computational and networking components. Our empirical evaluation demonstrates the impact of vehicle speed and user trajectory on system reliability. The results provide insights for network operators and smart city service providers, emphasizing the critical role of low-latency communication and how networks can enable relevant services for traffic safety.

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Accepted tutorial: Serverless Orchestration on the Edge-Cloud Continuum: Challenges and Solutions

Tutorial title: Serverless Orchestration on the Edge-Cloud Continuum: Challenges and Solutions

Venue: 16th ACM/SPEC International Conference on Performance Engineering (ICPE) (https://icpe2025.spec.org/)

We are happy to announce that our tutorial “Serverless Orchestration on the Edge-Cloud Continuum: Challenges and Solutions” (by Reza Farahani and Radu Prodan) has been accepted for ACM/SPEC ICPE 2025, which will take place in Torento, Canada, in May 2025.

 

Paper accepted: Dual Invariance Self-Training for Reliable Semi-Supervised Surgical Phase Recognition

Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Raphael Sznitman, Klaus Schoeffmann

Event: International Symposium on Biomedical Imaging (ISBI), April 14-17, 2025

Abstract: Accurate surgical phase recognition is crucial for advancing computer-assisted interventions, yet the scarcity of labeled data hinders training reliable deep learning models. Semi-supervised learning (SSL), particularly with pseudo-labeling, shows promise over fully supervised methods but often lacks reliable pseudo-label assessment mechanisms. To address this gap, we propose a novel SSL framework, Dual Invariance Self-Training (DIST), that incorporates both Temporal and Transformation Invariance to enhance surgical phase recognition. Our two-step self-training process dynamically selects reliable pseudo-labels, ensuring robust pseudo-supervision. Our approach mitigates the risk of noisy pseudo-labels, steering decision boundaries toward true data distribution and improving generalization to unseen data. Evaluations on Cataract and Cholec80 datasets show our method outperforms state-of-the-art SSL approaches, consistently surpassing both supervised and SSL baselines across various network architectures.

 

Paper accepted: For Empowerment’s Sake: Rethinking Video Game Adaptations Of Real-Life Experiences

Title: For Empowerment’s Sake: Rethinking Video Game Adaptations Of Real-Life Experiences

Author: Kseniia Harshina

Abstract:

In adapting real-life experiences into interactive narratives, video games have prioritized empathy-building as a central design goal (review in Schrier & Farber, 2021). This often involves crafting stories and gameplay mechanics that allow players without direct exposure to experiences such as mental illnesses, queerness, trauma, or forced migration—to better understand and emotionally connect with these realities. This focus on empathy can be seen as commendable and serving a critical purpose for broadening awareness and encouraging understanding. However, researchers, game developers and people with said experiences have raised critique against the rhetoric of empathy. Common arguments against these video games include minimizing the lived experiences, labeling them as “other” and promoting appropriation of affect (Ruberg, 2020). Prioritizing players without these experiences overlooks the opportunity to address the needs of those who have lived them. These players might not seek to feel empathy, but instead could benefit from video games that reflect their own stories and emotions, providing them with opportunities for healing, empowerment, and self-reflection. We believe there is a need for games designed explicitly with these audiences in mind and argue for an expansion of design goals in video game adaptations of real-life experiences including empowerment through feelings of catharsis.

Catharsis as a Mechanism for Empowerment

We view catharsis as a key component to fostering empowerment. Derived from Aristotelian concepts, catharsis refers to relieving tension and intense emotion by expressing them in a safe context (Kettles, 1995). To date, psychological research on catharsis in video games has largely focused on its connection to violent video games (e.g., Ferguson et al., 2014; Kersten & Greitemeyer, 2021). This limited focus has led to a gap in understanding how catharsis might operate as a constructive mechanism within video game narratives. We advocate reframing catharsis as a tool for processing emotions, promoting healing, and empowering players.

In our study of Reddit posts about Silent Hill 2, we examine how the game fosters cathartic experiences through its themes of trauma, guilt, and grief. Many players describe how the emotionally charged narrative and immersive world resonate with their own struggles, offering a space to process and confront difficult emotions. The ability to resonate with the game’s themes and process emotions through catharsis makes playing Silent Hill 2 an empowering experience for its players.

Co-Creating Stories: Designing for Affected Communities

To illustrate the potential of empowerment-focused video game adaptations of real-life experiences, we would like to present an ongoing participatory design project involving people with experiences of forced migration (Harshina, 2024; Harshina & Harbig, in press). Our project employs a collaborative methodology, combining surveys and iterative feedback sessions to co-create a game framework. By directly involving affected individuals, we aim to ensure that the resulting narratives authentically represent their lived experiences and emotional journeys.

Our design framework is structured around two distinct pathways: one tailored to players with lived experiences of forced migration and another aimed at promoting empathy among broader audiences. For the former group, the focus lies in crafting narratives and mechanics that provide cathartic, resilience-building experiences, reflecting their struggles and triumphs. For the latter, we emphasize creating opportunities for meaningful engagement and shared understanding through immersive storytelling. Additionally, we explore how a single game could serve as a bridge between these audiences, cultivating dialogue and mutual understanding. By combining empathy and empowerment within a single narrative, we envision games as a space where individuals with diverse perspectives can come together, challenge biases, and build solidarity.

Reimagining Video Game Adaptations

We argue for a reimagining of video game adaptations of real-life experiences that:
1. Ground narratives in authentic stories told by those directly affected.
2. Consider affected people as target audiences.
3. Strive for empowerment through cathartic and resilience-building experiences, rather than solely aiming for empathy.

Empowerment-focused narratives invite players to connect with their own emotions and stories. Our proposed dual-path approach—focusing on both empathy and empowerment—reframes how video games adapt real-life experiences. By centering affected individuals and embracing catharsis as a design principle, games can create meaningful connections across diverse audiences. We encourage game designers and researchers to collaborate with affected communities to craft narratives that resonate deeply, connecting storytelling, personal growth, and societal impact.

Paper accepted: Real-Time Quality- and Energy-Aware Bitrate Ladder Construction for Live Video Streaming

Real-Time Quality- and Energy-Aware Bitrate Ladder Construction for Live Video Streaming

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria), and Christian Timmerer (AAU, Austria)

Abstract: Live video streaming’s growing demand for high-quality content has resulted in significant energy consumption, creating challenges for sustainable media delivery. Traditional adaptive video streaming approaches rely on the over-provisioning of resources leading to a fixed bitrate ladder, which is often inefficient for the heterogeneous set of use cases and video content. Although dynamic approaches like per-title encoding optimize the bitrate ladder for each video, they mainly target video-on-demand to avoid latency and fail to address energy consumption. In this paper, we present LiveESTR, a method for building a quality- and energy-aware bitrate ladder for live video streaming. LiveESTR eliminates the need for exhaustive video encoding processes on the server side, ensuring that the bitrate ladder construction process is fast and energy efficient. A lightweight model for multi-label classification, along with a lookup table, is utilized to estimate the optimized resolution-bitrate pair in the bitrate ladder. Furthermore, both spatial and temporal resolutions are supported to achieve high energy savings while preserving compression efficiency. Therefore, a tunable parameter λ and a threshold τ are introduced to balance the trade-off between compression, quality, and energy efficiency. Experimental results show that LiveESTR reduces the encoder and decoder energy consumption by 74.6% and 29.7%, with only a 2.1% increase in Bjøntegaard Delta Rate (BD-Rate) compared to traditional per-title encoding. Furthermore, it is shown that by increasing λ to prioritize video quality, LiveESTR achieves 2.2% better compression efficiency in terms of BD-Rate while still reducing decoder energy consumption by 7.5%.

Paper accepted: Towards an Energy-Efficient Video Processing Tool with LLMs

Authors: Zoha Azimi (Alpen-Adria Universität Klagenfurt, Austria), Reza Farahani (Alpen-Adria Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria Universität Klagenfurt, Austria), Radu Prodan (Alpen-Adria Universität Klagenfurt, Austria)

Event: ACM 4th Mile-High Video Conference (MHV’25), 18–20 February 2025 | Denver, CO, USA

Abstract: Large language models (LLMs), the backbone of generative artificial intelligence (AI) like ChatGPT, have become more widely integrated in different fields, including multimedia. The rising number of conversational queries on such platforms now emits as much CO2 as everyday activities, leading to an exponential growth of energy consumption and underscoring urgent sustainability challenges. This short paper introduces an energy-aware LLM-based video processing tool. Employing open-source LLM models and techniques like fine-tuning and Retrieval-Augmented Generation (RAG), this tool recommends video processing commands and executes them in an energy-aware manner. Preliminary results show that it achieves reduced energy consumption per prompt compared to baselines.

 

Paper accepted: Enhancing Traffic Safety with AI and 6G: Latency Requirements and Real-Time Threat Detection

Authors: Kurt Horvath (University of Klagenfurt, Austria), Dragi Kimovski (University of Klagenfurt, Austria), Stojan Kitanov (Mother Theresa Universiy Skopje, Macedonia), Radu Prodan (University of Klagenfurt, Austria)

Event: 2025 10th International Conference on Information and Network Technologies (ICINT), March 12-14 2025, Melbourne (Australia)

Abstract: The rapid digitalization of urban infrastructure opens the path to smart cities, where IoT-enabled infrastructure enhances public safety and efficiency. This paper presents a 6G and AI-enabled framework for traffic safety enhancement, focusing on real-time detection and classification of emergency vehicles and leveraging 6G as the latest global communication standard. The system integrates sensor data acquisition, convolutional neural network-based threat detection, and user alert dissemination through various software modules of the use case. We define the latency requirements for such a system, segmenting the end-to-end latency into computational and networking components. Our empirical evaluation demonstrates the impact of vehicle speed and user trajectory on system reliability. The results provide insights for network operators and smart city service providers, emphasizing the critical role of low-latency communication and how networks can enable relevant services for traffic safety.

 

Hadi

Paper accepted: CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local Perspectives Using CLIP

CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local Perspectives Using CLIP

The IEEE International Symposium on Circuits and Systems (IEEE ISCAS 2025)

https://2025.ieee-iscas.org/

25–28 May 2025 // London, United Kingdom

Yirui Zeng (Cardiff University, UK), Jun Fu (Cardiff University), Hadi Amirpour (AAU, Austria), Huasheng Wang (Alibaba Group), Guanghui Yue (Shenzhen University, China), Hantao Liu (Cardiff University), Ying Chen (Alibaba Group), Wei Zhou (Cardiff University)

Abstract: Blind dehazed image quality assessment (BDQA), which aims to accurately predict the visual quality of dehazed images without any reference information, is essential for the evaluation, comparison, and optimization of image dehazing algorithms. Existing learning-based BDQA methods have achieved remarkable success, while the small scale of DQA datasets limits their performance. To address this issue, in this paper, we propose to adapt Contrastive Language-Image Pre-Training (CLIP), pre-trained on large-scale image-text pairs, to the BDQA task. Specifically, inspired by the fact that the human visual system understands images based on hierarchical features, we take global and local information of the dehazed image as the input of CLIP. To accurately map the input hierarchical information of dehazed images into the quality score, we tune both the vision branch and language branch of CLIP with prompt learning. Experimental results on two authentic DQA datasets demonstrate that our proposed approach, named CLIP-DQA, achieves more accurate quality predictions over existing BDQA methods.

Paper accepted: Multi-resolution Encoding for HTTP Adaptive Streaming using VVenC

Multi-resolution Encoding for HTTP Adaptive Streaming using VVenC

The IEEE International Symposium on Circuits and Systems (IEEE ISCAS 2025)

https://2025.ieee-iscas.org/

25–28 May 2025 // London, United Kingdom

Kamran Qureshi (AAU, Austria), Hadi Amirpour (AAU, Austria), Christian Timmerer (AAU, Austria)

Abstract: HTTP Adaptive Streaming (HAS) is a widely adopted method for delivering video content over the Internet, requiring each video to be encoded at multiple bitrates and resolution pairs, known as representations, to adapt to various network conditions and device capabilities. This multi-bitrate encoding introduces significant challenges due to the computational and time-intensive nature of encoding multiple representations. Conventional approaches often encode these videos independently without leveraging similarities between different representations of the same input video. This paper proposes an accelerated multi-resolution encoding strategy that utilizes representations of lower resolutions as references to speed up the encoding of higher resolutions when using Versatile Video Coding (VVC); specifically in VVenC, an optimized open-source software implementation. For multi-resolution encoding, a mid-bitrate representation serves as the reference, allowing interpolated encoded partition data to efficiently guide the partitioning process in higher resolutions. The proposed approach uses shared encoding information to reduce redundant calculations, thereby optimizing the partitioning decisions. Experimental results demonstrate that the proposed technique achieves a reduction of up to 17% compared to medium preset in encoding time across videos of varying complexities with minimal BDBR/BDT of 0.12 compared to the fast preset.