In-Band Quality Notification from Users to ISPs

In-Band Quality Notification from Users to ISPs

IEEE 13th International Conference on Cloud Networking (CloudNet)

27–29 November 2024 // Rio de Janeiro, Brazil

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Leonardo Peroni (UC3M, Spain); Sergey Gorinsky (IMDEA Networks Institute, Spain); Farzad Tashtarian (Alpen-Adria Universität Klagenfurt, Austria)

Abstract: While ISPs (Internet service providers) strive to improve QoE (quality of experience) for end users, end-to-end traffic encryption by OTT (over-the-top) providers undermines independent inference of QoE by an ISP. Due to the economic and technological complexity of the modern Internet, ISP-side QoE inference based on OTT assistance or out-of-band signaling sees low adoption. This paper presents IQN (in-band quality notification), a novel mechanism for signaling QoE impairments from an automated agent on the end-user device to the server-to-client ISP responsible for QoE-impairing congestion. Compatible with multi-ISP paths, asymmetric routing, and other Internet realities, IQN does not require OTT support and induces the OTT server to emit distinctive packet patterns that encode QoE information, enabling ISPs to infer QoE by monitoring these patterns in network traffic. We develop a prototype system, YouStall, which applies IQN signaling to ISP-side inference of YouTube stalls.
Cloud-based experiments with YouStall on YouTube Live streams validate IQN’s feasibility and effectiveness, demonstrating its potential for accurate user-assisted ISP-side QoE inference from encrypted traffic in real Internet environments.

Index Terms— End user, QoE, ISP, QoE-impairment inference, OTT provider, end-to-end traffic encryption, in-band signaling.

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Characterizing the Geometric Complexity of G-PCC Compressed Point Clouds

Characterizing the Geometric Complexity of G-PCC Compressed Point Clouds

IEEE Visual Communications and Image Processing (IEEE VCIP 2024)

Tokyo, Japan, December 8-11, 2024

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Annalisa Gallina (UNIPD, Italy), Hadi Amirpour (AAU, Austria), Sara Baldoni (UNIPD, Italy), Giuseppe Valenzise (UPSaclay, France), Federica Battisti (UNIPD, Italy).

Abstract: Measuring the complexity of visual content is crucial in various applications, such as selecting sources to test processing algorithms, designing subjective studies, and efficiently determining the appropriate encoding parameters and bandwidth allocation for streaming. While spatial and temporal complexity measures exist for 2D videos, a geometric complexity measure for 3D content is still lacking.
In this paper, we present the first study to characterize the geometric complexity of 3D point clouds. Inspired by existing complexity measures, we propose several compression-based definitions of geometric complexity derived from the rate-distortion curves obtained by compressing a dataset of point clouds using G-PCC. Additionally, we introduce density-based and geometry-based descriptors to predict complexity. Our initial results show that even simple density measures can accurately predict the geometric complexity of point clouds.

Index Terms— Point cloud, complexity, compression, G-PCC.

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Energy-Efficient Video Streaming: A Study on Bit Depth and Color Subsampling

Energy-Efficient Video Streaming: A Study on Bit Depth and Color Subsampling

IEEE Visual Communications and Image Processing (IEEE VCIP 2024)

Tokyo, Japan, December 8-11, 2024

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Hadi Amirpour (AAU, Austria), Lingfeng Qu (Guangzhou University, China), Jong Hwan Ko (SKKU, South Korea), Cosmin Stejerean (Meta, USA), Christian Timmerer (AAU, Austria)

Abstract: As video dimensions — including resolution, frame rate, and bit depth — increase, a larger bitrate is required to maintain a higher Quality of Experience (QoE). While videos are often optimized for resolution and frame rate to improve compression and energy efficiency, the impact of color space is often overlooked.
Larger color spaces are essential for avoiding color banding and delivering High Dynamic Range (HDR) content with richer, more accurate colors, although this comes at the cost of higher processing energy. This paper investigates the effects of bit depth and color subsampling on video compression efficiency and energy consumption. By analyzing different bit depths and subsampling schemes, we aim to determine optimized settings that balance compression efficiency with energy consumption, ultimately contributing to more sustainable and high-quality video delivery. We evaluate both encoding and decoding energy consumption and assess the quality of videos using various metrics including PSNR, VMAF, ColorVideoVDP, and CAMBI. Our findings offer valuable insights for video codec developers and content providers aiming to improve the performance and environmental footprint of their video streaming services.

Index Terms— Video encoding, video decoding, video quality, bit depth, color subsampling, energy.

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MVCD: Multi-Dimensional Video Compression Dataset

MVCD: Multi-Dimensional Video Compression Dataset

IEEE Visual Communications and Image Processing (IEEE VCIP 2024)

Tokyo, Japan, December 8-11, 2024

[PDF]

Hadi Amirpour (AAU, Austria), Mohammad Ghasempour (AAU, Austria), Farzad Tashtarian (AAU, Austria), Ahmed Telili (TII, UAE), Samira Afzal (AAU, Austria), Wassim Hamidouche (INSA, France), Christian Timmerer (AAU, Austria)

Abstract: In the field of video streaming, the optimization of video encoding and decoding processes is crucial for delivering high-quality video content. Given the growing concern about carbon dioxide emissions, it is equally necessary to consider the energy consumption associated with video streaming. Therefore, to take advantage of machine learning techniques for optimizing video delivery, a dataset encompassing the energy consumption of the encoding and decoding process is needed. This paper introduces a comprehensive dataset featuring diverse video content, encoded and decoded using various codecs and spanning different devices. The dataset includes 1000 videos encoded with four resolutions (2160p, 1080p, 720p, and 540p) at two frame rates (30 fps and 60 fps), resulting in eight unique encodings for each video. Each video is further encoded with four different codecs — AVC (libx264), HEVC (libx265), AV1 (libsvtav1), and VVC (VVenC) — at four quality levels defined by QPs of 22, 27, 32, and 37. In addition, for AV1, three additional QPs of 35, 46, and 55 are considered. We measure both encoding and decoding time and energy consumption on various devices to provide a comprehensive evaluation, employing various metrics and tools. Additionally, we assess encoding bitrate and quality using quality metrics such as PSNR, SSIM, MS-SSIM, and VMAF. All data and the reproduction commands and scripts have been made publicly available as part of the dataset, which can be used for various applications such as rate and quality control, resource allocation, and energy-efficient streaming.

Dataset URL: https://github.com/cd-athena/MVCD

Index Terms— Video encoding, decoding, energy, complexity, quality.

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Energy-Quality-aware Variable Framerate Pareto-Front for Adaptive Video Streaming

Energy-Quality-aware Variable Framerate Pareto-Front for Adaptive Video Streaming

IEEE Visual Communications and Image Processing (IEEE VCIP 2024)

Tokyo, Japan, December 8-11, 2024

[PDF]

Prajit T Rajendran (Universite Paris-Saclay), Samira Afzal (Alpen-Adria-Universität Klagenfurt), Vignesh V Menon (Fraunhofer HHI), Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: Optimizing framerate for a given bitrate-spatial resolution pair in adaptive video streaming is essential to maintain perceptual quality while considering decoding complexity. Low framerates at low bitrates reduce compression artifacts and decrease decoding energy. We propose a novel method, Decoding-complexity aware Framerate Prediction (DECODRA), which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints. DECODRA dynamically adjusts the framerate based on current bitrate and spatial resolution, balancing trade-offs between framerate, perceptual quality, and decoding complexity. Extensive experimentation with the Inter-4K dataset demonstrates DECODRA’s effectiveness, yielding an average PSNR and VMAF increase of 0.87 dB and 5.14 points, respectively, for the same bitrate compared to the default 60 fps encoding. Additionally, DECODRA achieves an average reduction in decoding energy consumption of 13.27 %, enhancing the viewing experience, extending mobile device battery life, and reducing the energy footprint of streaming services.

Index Terms—Adaptive video streaming; Decoding complexity; Framerate optimization; Perceptual quality; Decoding energy consumption

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ACM Mile-High Video Conference 2025: Call for Contributions

MHV 2025: ACM Mile-High Video Conference 2025
Call for Contributions
February 18-20, 2025, The Cable Center, Denver, Colorado
https://www.mile-high.video/
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University assistant predoctoral (all genders welcome) (in German: Universitätsassistent:in)

The University of Klagenfurt, with approximately 1,500 employees and over 12,000 students, is located in the Alps-Adriatic region and consistently achieves excellent placements in rankings. The motto “per aspera ad astra” underscores our firm commitment to the pursuit of excellence in all research, teaching, and university management activities. The principles of equality, diversity, health, sustainability, and compatibility of work and family life serve as the foundation for our work at the university.

The University of Klagenfurt is pleased to announce the following open position at the Department of Information Technology at the Faculty of Technical Sciences with an expected starting date of November 4, 2024:

University assistant predoctoral (all genders welcome) (in German: Universitätsassistent:in)

within the Ada Lovelace Programme (project title: Streaming of Holographic Content and its Impact on the Quality of Experience).

  • Level of employment: 100 % (40 hours/week)
  • Minimum salary: € 50,103.20 per annum (gross); Classification according to collective agreement: B1
  • Contract duration: 4 years
  • Application deadline: by September 11, 2024
  • Reference code: 348/24

Tasks and responsibilities:

  • Autonomous scientific work, including the publication of research articles in the fields of coding and streaming of holographic content, Quality of Experience (QoE), and behavioural sciences
  • Conducting independent scientific research with the aim of submitting a dissertation and acquiring a doctoral degree in technical sciences
  • Teaching exercises and lab courses (e.g., in the computer science Bachelor’s or/and Master’s programme)
  • Participating in research projects of the department, especially within the Ada Lovelace Programme (Streaming of Holographic Content and its Impact on the Quality of Experience)
  • Mentoring students
  • Assisting in public relations activities, science to public communication, and extra-curricular events of the department and the faculty

Prerequisites for the appointment:

  • Completed Diploma or Master’s degree from a recognized university in the field of computer science, information and communications engineering, electrical engineering, or related fields. The completion of this degree must be fulfilled no later than two weeks before the starting date; hence, the last possible deadline for meeting this requirement is October 20, 2024
  • Strong background in one or more of the following fields: multimedia systems (i.e., video/holographic content coding/streaming, Quality of Experience) and empirical research methods (i.e., statistical methods, interdisciplinary research with behavioural sciences)
  • Fluent in written and spoken English
  • Programming experience in multimedia systems

Additional desired qualifications:

  • Experience with scientific publications or presentations
  • Experience in interdisciplinary research projects, ideally in the behavioural sciences, as the project involves empirical research
  • Excellent ability to work with teams
  • Scientific curiosity and enthusiasm for research in multimedia systems and empirical research

The doctoral student will be co-supervised by Christian Timmerer, Heather Foran, and Hadi Amirpour.

Our offer:

This position serves the purposes of the vocational and scientific education of graduates of Master’s or Diploma degree programmes and sets the goal of completing a Doctoral degree / a Ph.D. in Technical Sciences. Therefore, applications by persons who have already completed a subject-specific doctoral degree or a subject-relevant Ph.D. program cannot be considered.

The employment contract is concluded for the position of university assistant (predoctoral) and stipulates a starting salary of € 3,578.80 gross per month (14 times a year; previous experience deemed relevant to the job can be recognized in accordance with the collective agreement).

The University of Klagenfurt also offers:

  • Personal and professional advanced training courses, management, and career coaching
  • Numerous attractive additional benefits, see also https://jobs.aau.at/en/the-university-as-employer/
  • Diversity- and family-friendly university culture
  • The opportunity to live and work in the attractive Alps-Adriatic region with a wide range of leisure activities in the spheres of culture, nature, and sports

The application:

If you are interested in this position, please apply in English by providing the following documents:

  • Letter of application/cover letter including motivation statement for the given position
  • Curriculum vitae (with clear information about the degrees, including date/place/grade, the experience acquired, the thesis title, the list of publications (if any), and any other relevant information)
  • Copy of the degree certificates and transcripts of the courses
  • Any certificates that can prove the fulfilment of the required and additional qualifications listed above (e.g., the submission of the final thesis if required by the degree programme, copy of publications, programming skills certificates, language skills certificates, etc.)
  • Final thesis or other study-related written work (like seminar reports) or excerpts thereof
  • If an applicant has not received the Diploma or Master’s degree by the application deadline, the applicant should provide a declaration, written either by a supervisor or by the candidate themselves, on the feasibility of finishing the Diploma or Master’s degree by October 30, 2024 at the latest.

To apply, please select the position with the reference code 348/24 in the category “Scientific Staff” using the link “Apply for this position” in the job portal at jobs.aau.at/en/.

Candidates must furnish proof that they meet the required qualifications by October 20, 2024 at the latest.

For further information on this specific vacancy, please contact Univ.-Prof. DI Dr. Christian Timmerer (christian.timmerer@aau.at). General information about the university as an employer can be found at https://jobs.aau.at/en/the-university-as-employer/. At the University of Klagenfurt, recruitment and staff matters are accompanied not only by the authority responsible for the recruitment procedure but also by the Equal Opportunities Working Group and, if necessary, by the Representative for Disabled Persons.

The University of Klagenfurt aims to increase the proportion of women and, therefore, invites explicitly qualified women to apply for the position. Where the qualification is equivalent, women will be given preferential consideration.

People with disabilities or chronic diseases, who fulfill the requirements, are particularly encouraged to apply.

Travel and accommodation costs incurred during the application process will not be refunded. Translations into other languages shall serve informational purposes only. Solely the version advertised in the University Bulletin (Mitteilungsblatt) shall be legally binding.

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