Patent Approval for “Efficient two-pass encoding scheme for adaptive live streaming”

Posted on January 7, 2026 by

Efficient two-pass encoding scheme for adaptive live streaming

US Patent

[PDF]

Vignesh Menon (Alpen-Adria-Universität Klagenfurt, Austria), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

 

Abstract: Techniques for efficient two-pass encoding for live streaming are described herein. A method for efficient two-pass encoding may include extracting low-complexity features of a video segment, predicting an optimized constant rate factor (CRF) for the video segment using the low-complexity features, and encoding the video segment with the optimized CRF at a target bitrate. A system for efficient two-pass encoding may include a feature extraction module configured to extract low-complexity features from a video segment, a neural network configured to predict an optimized CRF as a function of the low-complexity features and a target bitrate, and an encoder configured to encode the video segment using the optimized CRF at the target bitrate.

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X4-MATCH: Sustainable Prediction-based Distribution of Video Encoding on Cloud and Edge

Posted on December 23, 2025 by

X4-MATCH: Sustainable Prediction-based Distribution of Video Encoding on Cloud and Edge

40th IEEE International Parallel & Distributed Processing Symposium

May 25-29, 2026
New Orleans, USA
https://www.ipdps.org/

[PDF]

Samira Afzal (Baylor University), Narges Mehran (University of Salzburg), Andrew C. Freeman (Baylor University), Manuel Hoi (University of Klagenfurt), Armin Lachini (University of Klagenfurt), Radu Prodan (University of Innsbruck), Christian Timmerer (University of Klagenfurt)

Abstract: The rapid expansion of video traffic has made it one of the most energy-intensive workloads on cloud and edge infrastructures. As encoding remains essential for streaming, gaming, and immersive applications, efficient task scheduling is required to balance service quality, cost efficiency, and sustainability. In this work, we propose a sustainable scheduling framework that integrates machine learning–based performance prediction with game-theoretic matching (X4-MATCH), designed to distribute video encoding workloads across cloud–edge infrastructures. The framework formulates four key performance metrics, including processing and transmission time, price, energy use, and CO2 emissions, as optimization objectives to balance performance and sustainability goals. This method leverages the eXtra-trees regressor model to predict performance metrics for video encoding tasks, integrated with a Matching theory-based resource allocation strategy to efficiently utilize computational resources across cloud and edge computing resources. We experimentally validate the effectiveness of X4-MATCH on a real-world testbed incorporating Amazon Web Services (AWS) cloud virtual machines/instances and local edge servers. Results show that X4-MATCH outperforms state-of-the-art methods by reducing total time by 63.3%, price by 54.2%, and energy by 56.8%.

Index Terms: Video encoding, energy efficiency, cloud and edge, matching theory, extra-trees regressor

X4-MATCH distribution overview

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Eye-Tracking, Quality Assessment, and QoE Prediction Models for Point Cloud Videos: Extended Analysis of the ComPEQ-MR Dataset

Posted on November 4, 2025 by

Eye-Tracking, Quality Assessment, and QoE Prediction Models for Point Cloud Videos: Extended Analysis of the ComPEQ-MR Dataset

IEEE Access 2025

[PDF]

Shivi Vats (AAU, Austria), Minh Nguyen (AAU, Austria)*, Christian Timmerer (AAU, Austria), Hermann Hellwagner (AAU, Austria)

Abstract: Point cloud videos, also termed dynamic point clouds (DPCs), have the potential to provide immersive experiences with six degrees of freedom (6DoF). However, there are still several open issues in understanding the Quality of Experience (QoE) and visual attention of end users while experiencing 6DoF volumetric videos. For instance, the quality impact of compressing DPCs, which requires a significant amount of both time and computational resources, needs further investigation. Also, QoE prediction models for DPCs in 6DoF have rarely been developed due to the lack of visual quality databases. Furthermore, visual attention in 6DoF is hardly explored, which impedes research into more sophisticated approaches for adaptive streaming of DPCs. In this paper, we review and analyze in detail the open-source Compressed Point cloud dataset with Eye-tracking and Quality assessment in Mixed Reality (ComPEQ–MR). The dataset, initially presented in [24], comprises 4 uncompressed (raw) DPCs as well as compressed versions processed by Moving Picture Experts Group (MPEG) reference tools (i.e., VPCC and 2 GPCC variants). The dataset includes eye-tracking data of 41 study participants watching the raw DPCs with 6DoF, yielding 164 visual attention maps. We analyze this data and present head and gaze movement results here. The dataset also includes results from subjective tests conducted to assess the quality of the DPCs, each both uncompressed and compressed with 12 levels of distortion, resulting in 2132 quality scores. This work presents the QoE performance results of the compression techniques, the factors with significant impact on participant ratings, and the correlation of the objective Peak Signal-to-Noise Ratio (PSNR) metrics with Mean Opinion Scores (MOS). The results indicate superior performance of the VPCC codec as well as significant variations in quality ratings based on codec choice, bitrate, and quality/distortion level, providing insights for optimizing point cloud video compression in MR applications. Finally, making use of the subjective scores, we trained and evaluated models for QoE prediction for DPCs compressed using the pertinent MPEG tools. We present the models and their prediction results, noting that the fine-tuned ITU-T P.1203 models exhibit good correlation with the subjective ratings. The dataset is available at https://ftp.itec.aau.at/datasets/ComPEQ-MR/.

* Minh Nguyen is currently a Research Associate at Fraunhofer FOKUS, Germany but this work was done when he was working for AAU.
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STEP-MR: A Subjective Testing and Eye-Tracking Platform for Dynamic Point Clouds in Mixed Reality

Posted on November 4, 2025 by

STEP-MR: A Subjective Testing and Eye-Tracking Platform for Dynamic Point Clouds in Mixed Reality

MMM 2026

January 29 – January 31, 2026

Prague, Czech Republic

[PDF, Poster]

Shivi Vats (AAU, Austria), Christian Timmerer (AAU, Austria), Hermann Hellwagner (AAU, Austria)

Abstract: The use of point cloud (PC) streaming in mixed reality (MR) environments is of particular interest due to the immersiveness and the six degrees of freedom (6DoF) provided by the 3D content. However, this immersiveness requires significant bandwidth. Innovative solutions have been developed to address these challenges, such as PC compression and/or spatially tiling the PC to stream different portions at different quality levels. This paper presents a brief overview of a Subjective Testing and Eye-tracking Platform for dynamic point clouds in Mixed Reality (STEP-MR) for the Microsoft HoloLens 2. STEP-MR was used to conduct subjective tests (described in [1]) with 41 participants, yielding over 2000 responses and more than 150 visual attention maps, the results of which can be used, among other things, to improve dynamic (animated) point cloud streaming solutions mentioned above. Building on our previous platform, the new version now enables eye-tracking tests, including calibration and heatmap generation. Additionally, STEP-MR features modifications to the subjective tests’ functionality, such as a new rating scale and adaptability to participant movement during the tests, along with other user experience changes.

[1] Nguyen, M., Vats, S., Zhou, X., Viola, I., Cesar, P., Timmerer, C., & Hellwagner, H. (2024). ComPEQ-MR: Compressed Point Cloud Dataset with Eye Tracking and Quality Assessment in Mixed Reality. Proceedings of the 15th ACM Multimedia Systems Conference, 367–373. https://doi.org/10.1145/3625468.3652182
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Patent Approval for “Energy-aware ABR Algorithm for Green HTTP Adaptive Video Streaming”

Posted on October 30, 2025 by

Energy-aware ABR Algorithm for Green HTTP Adaptive Video Streaming

US Patent

[PDF]

Daniele Lorenzi (Alpen-Adria-Universität Klagenfurt, Austria), Minh Nguyen (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), Martin Smole (Bitmovin), Roland Kersche (Bitmovin), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: Techniques for implementing an energy-aware ABR algorithm for adaptive streaming may include determining whether a buffer level is less than a threshold buffer, selecting a lowest bitrate representation for playback of a segment of a video in a conservative mode when the buffer level is less than the threshold buffer, calculating a cost of a representation in an operative mode when the buffer level exceeds the threshold buffer, the cost of the representation comprising a weighted sum of a throughput cost, a buffer cost, a quality cost, and optionally also an energy cost, selecting a bitrate for a next segment of the video based on the cost of the representation, and providing to a client device a selected representation. The energy-aware ABR algorithm may be implemented when an ECO mode is selected in a client device.

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SEED: Energy and Emission Estimation Dataset for Adaptive Video Streaming

Posted on September 27, 2025 by

SEED: Energy and Emission Estimation Dataset for Adaptive Video Streaming

IEEE VCIP 2025

December 1 – December 4, 2025

Klagenfurt, Austria

[PDF]

Samira Afzal (Baylor University), Narges Mehran (Salzburg Research Forschungsgesellschaft mbH), Farzad Tashtarian (AAU, Austria), Andrew C. Freeman (Baylor University), Radu Prodan (University of Innsbruck), Christian Timmerer (AAU, Austria)

Abstract: The environmental impact of video streaming is gaining more attention due to its growing share in global internet traffic and energy consumption. To support accurate and transparent sustainability assessments, we present SEED (Streaming Energy and Emission Dataset)}: an open dataset for estimating energy usage and CO2 emissions in adaptive video streaming. SEED comprises over 500 video segments. It provides segment-level measurements of energy consumption and emissions for two primary stages: provisioning, which encompasses encoding and storage on cloud infrastructure, and end-user consumption, including network interface retrieval, video decoding, and display on end-user devices. The dataset covers multiple codecs (AVC, HEVC), resolutions, bitrates, cloud instance types, and geographic regions, reflecting real-world variations in computing efficiency and regional carbon intensity. By combining empirical benchmarks with component-level energy models, \dataset{} enables detailed analysis and supports the development of energy- and emission-aware adaptive bitrate (ABR) algorithms. The dataset is publicly available at: https://github.com/cd-athena/SEED.

SEED is available at: https://github.com/cd-athena/SEED

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Resolution vs Quantization: A Trade-Off for Hologram Compression

Posted on September 23, 2025 by

Resolution vs Quantization: A Trade-Off for Hologram Compression

Ayman Alkhateeb∗, Hadi Amirpour∗,Christian Timmerer∗

Alpen-Adria-Universität , Klagenfurt, Austria

PCS 2025  Achen , German

Holographic imaging offers a path to true three-dimensional visualization for applications such as augmented andvirtual reality, but the immense data size of high-quality holo-grams prevents their practical adoption. This paper investigatesa pre-processing strategy to improve the compression of holo-graphic data using the High-Efficiency Video Coding (HEVC) standard. By downsampling the hologram before encoding andsubsequently upsampling it after decoding, we demonstrate thatit is possible to achieve better reconstruction quality at lowbitrates compared to encoding the full-resolution data. Thiscounterintuitive result basically comes from the reduction inspatial complexity, which allows the HEVC encoder to allocate more bits to preserving critical high-frequency information that would otherwise be lost. Although the hologram phase is highly sensitive to scaling,the overall perceptual quality improves at bitrates below 1 Bpp, with gains of approximately 0.1 in SSIM and 0.015 in VIF. Our work underscores a critical principle in holographic codec design: optimizing the trade-off between spatial complexity and quantization error is key to maximizing reconstruction quality, especially in bandwidth-constrained environments.

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