Predicting Encoding Energy from Low-Pass Anchors for Green Video Streaming

Authors: Zoha Azimi (AAU, Austria), Reza Farahani (AAU, Austria), Vignesh V Menon (Fraunhofer HHI, Berlin), Christian Timmerer (AAU, Austria)

Event: 1st International Workshop on Intelligent and Scalable Systems Across the Computing Continuum (ScaleSys ’25), November 18, 2025, Vienna, Austria, https://scalesys2025.itec.aau.at/

Abstract:  

Video streaming now represents the dominant share of Internet traffic, as ever-higher-resolution content is distributed across a growing range of heterogeneous devices to sustain user Quality of Experience (QoE). However, this trend raises significant concerns about energy efficiency and carbon emissions, requiring methods to provide a trade-off between energy and QoE. This paper proposes a lightweight energy prediction method that estimates the energy consumption of high-resolution video encodings using reference encodings generated at lower resolutions (so-called anchors), eliminating the need for exhaustive per-segment energy measurements, a process that is infeasible at scale. We automatically select encoding parameters, such as resolution and quantization parameter (QP), to achieve substantial energy savings while maintaining perceptual quality, as measured by the Video Multimethod Fusion Assessment (VMAF), within acceptable limits. We implement and evaluate our approach with the open-source VVenC encoder on 100 video sequences from the Inter4K dataset across multiple encoding settings. Results show that, for an average VMAF score reduction of only 1.68, which stays below the Just Noticeable Difference (JND)
threshold, our method achieves 51.22 % encoding energy savings and 53.54 % decoding energy savings compared to a scenario with no quality degradation.

 

Title: GynSurg: A Comprehensive Gynecology Laparoscopic Surgery Dataset
Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Leonie Peschek, Matteo Munari, Heinrich Husslein, Raphael Sznitman, Klaus Schoeffmann
Venue: ACM Multimedia 2025, 27 – 31 October 2025, Dublin, Ireland

Abstract: Recent advances in deep learning have transformed computer-assisted intervention and surgical video analysis, driving improvements not only in surgical training, intraoperative decision support, and patient outcomes, but also in postoperative documentation and surgical discovery. Central to these developments is the availability of large, high-quality annotated datasets. In gynecologic laparoscopy, surgical scene understanding and action recognition are fundamental for building intelligent systems that assist surgeons during operations and provide deeper analysis after surgery. However, existing datasets are often limited by small scale, narrow task focus, or insufficiently detailed annotations, limiting their utility for comprehensive, end-to-end workflow analysis. To address these limitations, we introduce GynSurg, the largest and most diverse multitask dataset for gynecologic laparoscopic surgery to date. GynSurg provides rich annotations across multiple tasks, supporting applications in action recognition, semantic segmentation, surgical documentation, and discovery of novel procedural insights. We demonstrate the dataset’s quality and versatility by benchmarking state-of-the-art models under a standardized training protocol. To accelerate progress in the field, we publicly release the GynSurg dataset and its annotations (https://ftp.itec.aau.at/datasets/GynSurge/).

Title: Agentic Edge Intelligence: A Research Agenda

Authors: Lauri Lovén, Reza Farahani, Ilir Murturi, Stephan Sigg, Schahram Dustdar

Abstract: Agentic AI is rapidly transforming autonomous decision-making, yet its deployment across the edge-cloud continuum remains poorly understood. This paper introduces the concept of agentic edge intelligence, an emerging paradigm in which autonomous agents operate across the computing continuum to negotiate computational resources, data, and services within dynamic digital marketplaces. We position this concept at the intersection of edge intelligence, multi-agent systems, and computational economics, where distributed decision-making replaces centralized orchestration. The paper outlines key research challenges, including scalability, interoperability, market stability, and ethical governance, and proposes a research agenda addressing theoretical, architectural, and societal dimensions. By integrating mechanism design with trustworthy AI and edge computing, the real-time AI economy envisions a self-organizing infrastructure for efficient, transparent, and equitable resource exchange in future digital ecosystems.

Venue: International Workshop on Intelligent Systems and Paradigms for Next Generation Computing Evolution (INSPIRE 2025) in conjunction with the 18th IEEE/ACM Utility and Cloud Computing Conference (UCC)

Title: Toward Sustainability-Aware LLM Inference on Edge Clusters

Authors: Kolichala Rajashekar, Nafiseh Sharghivand, Radu Prodan, Reza Farahani

Abstract: Large language models (LLMs) require substantial computational resources, leading to significant carbon emissions and operational costs. Although training is energy-intensive, the long-term environmental burden arises from inference, amplified by the massive global query volume. Cloud-based inference offers scalability but suffers from latency and bandwidth constraints due to centralized processing and continuous data transfer. Edge clusters instead can mitigate these limitations by enabling localized execution, yet they face trade-offs between performance, energy efficiency, and device constraints. This short paper presents a sustainability-aware LLM inference for edge clusters comprising NVIDIA Jetson Orin NX (8GB) and Nvidia Ada 2000 (16GB) devices. It aims to balance inference latency and carbon footprint through carbon- and latency-aware routing strategies, guided by empirical benchmarking of energy consumption and execution time across diverse prompts and batch (i.e., group of prompts) configurations. We compared baseline greedy strategies to carbon-aware and latency-aware strategies in prompt routing to specific hardware based on benchmarking information. Experimental evaluation shows that a batch size of four prompts achieves a trade-off between throughput, energy efficiency, while larger batches risk GPU memory saturation.

Venue: International Workshop on Intelligent and Scalable Systems across the Computing Continuum (ScaleSys 2025) in conjunction with the 15th International Conference on the Internet of Things (loT 2025)