IEEE Access, A Multidisciplinary, Open-access Journal of the IEEE

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Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Daniele Lorenzi (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

(*) Minh Nguyen and Daniele Lorenzi contributed equally to this work

dofp+_motivation

Abstract: HTTP Adaptive Streaming (HAS) solutions use various adaptive bitrate (ABR) algorithms to select suitable video qualities with the objective of coping with the variations of network connections. HTTP has been evolving with various versions and provides more and more features. Most of the existing ABR algorithms do not significantly benefit from the HTTP development when they are merely supported by the most recent HTTP version. An open research question is “How can new features of the recent HTTP versions be used to enhance the performance of HAS?” To address this question, in this paper, we introduce Days of Future Past+ (DoFP+ for short), a heuristic algorithm that takes advantage of the features of the latest HTTP version, HTTP/3, to provide high Quality of Experience (QoE) to the viewers. DoFP+ leverages HTTP/3 features, including (i) stream multiplexing, (ii) stream priority, and (iii) request cancellation to upgrade low-quality segments in the player buffer while downloading the next segment. The qualities of those segments are selected based on an objective function and throughput constraints. The objective function takes into account two factors, namely the (i) average bitrate and the (ii) video instability of the considered set of segments. We also examine different strategies of download order for those segments to optimize the QoE in limited resources scenarios. The experimental results show an improvement in QoE by up to 33% while the number of stalls and stall duration for DoFP+ are reduced by 86% and 92%, respectively, compared to state-of-the-art ABR schemes. In addition, DoFP+ saves on average up to 16% downloaded data across all test videos. Also, we find that downloading segments sequentially brings more benefits for retransmissions than concurrent downloads; and lower-quality segments should be upgraded before other segments to gain more QoE improvement. Our source code has been published for reproducibility at https://github.com/cd-athena/DoFP-Plus.

Keywords: HTTP/3, ABR algorithm, QoE, HAS, DASH

DataCloud will research and develop novel methods to support the complete lifecycle of #BigData pipelines processing.Follow us to know more about #datacloud2020

On the first two days of October 2022, Sebastian Uitz and Michael Steinkellner presented their game “A Webbing Journey” at the Vienna Comic Con 2022 (VIECC 2022). The new demo level from Gamescom was incorporated into the existing demo and replaced the old first level. In addition, the art style was updated, and the new arachnophobia mode was added to help everyone enjoy our game, even those afraid of spiders. This event was the biggest Austrian event we attended yet, with over 35.000 visitors. Next to meeting some cool Austrian game devs and having many happy players, we also had an interview with the FM4 Spielekammerl about our game.

From Wednesday, 24.08.2022, until Sunday, 28.08.2022, players from all over the world played our game “A Webbing Journey” at Gamescom 2022 in Cologne. Our booth was run by Sebastian Uitz, Manuel Santner, and Fabian Schober, and we have been one of the only two Austrian games in the indie area this year. The second one is Gibbon from Broken Rules. The size of the Gamescom is just crazy compared to the Austrian events, with over 250.000 people attending this year. We prepared a new demo level for this event, and the feedback was great, as always. In addition, we made valuable connections to the games industry through other game devs, publishers, and the press.

The futurezone Awards 2022 are looking for the most innovative ideas and projects. Three finalists (each category) have now been selected from the numerous submissions.

ITEC was nominated for two projects: 5G Virtual Realities and GAIA (together with Bitmovin).

The ADAPT workshop took place in Macau, China, on 09 Oct 2022 at the IEEE ITSC conference with more than 50 international attendees! The ADAPT-Team has successfully participated!

 

Bitmovin, a leading provider of video streaming infrastructure, and the University of Klagenfurt announced they will collaborate on a two-year joint research project worth €3.3million to develop a climate-friendly adaptive video streaming platform called ‘GAIA’. The Austrian Research Promotion Agency (FFG) will co-fund the project, providing an initial €460,000 in funding for the first year.

Project ‘GAIA’ aims to identify ways to improve sustainability and reduce energy consumption across the end-to-end video streaming chain by

  • Enabling energy awareness and accountability, including benchmarking and predicting energy consumption and greenhouse gas emissions across the entire delivery chain, from content creation and server-side encoding to video transmission and client-side rendering.
  • Reducing energy consumption and greenhouse gas emissions through advanced analytics and optimizations on all phases of the video delivery chain.

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At Christian Doppler laboratory ATHENA, we offer an internship*) for 2023 for Master Students and we kindly request your applications until the 20th of January 2023 with the following data (in German or English):

  • CV
  • Record of study/transcript (“Studienerfolgsnachweis”)

*) A 3 months period in 2023 (with an exact time slot to be discussed) with the possibility to spend up to 1-month at the industrial partner; 20h per week “Universitäts-KV, Verwendungsgruppe C1, studentische Hilfskraft”

Please send your application by email to nina.stiller@aau.at.

About ATHENA: The Christian Doppler laboratory ATHENA (AdapTive Streaming over HTTP and Emerging Networked MultimediA Services) is jointly proposed by the Institute of Information Technology (ITEC; http://itec.aau.at) at Alpen-Adria-Universität Klagenfurt (AAU) and Bitmovin GmbH (https://bitmovin.com) to address current and future research and deployment challenges of HAS and emerging streaming methods. AAU (ITEC) has been working on adaptive video streaming for more than a decade, has a proven record of successful research projects and publications in the field, and has been actively contributing to MPEG standardization for many years, including MPEG-DASH; Bitmovin is a video streaming software company founded by ITEC researchers in 2013 and has developed highly successful, global R&D and sales activities and a world-wide customer base since then.

The aim of ATHENA is to research and develop novel paradigms, approaches, (prototype) tools, and evaluation results for the phases

  1. multimedia content provisioning,
  2. content delivery, and
  3. content consumption in the media delivery chain as well as for
  4. end-to-end aspects, with a focus on, but not being limited to, HTTP Adaptive Streaming (HAS).

The new approaches and insights are to enable Bitmovin to build innovative applications and services to account for the steadily increasing and changing multimedia traffic on the Internet.

Vignesh V Menon

2022 Picture Coding Symposium (PCS)

December 7-9, 2022 | San Jose, CA, USA

Conference Website

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Prajit T Rajendran (Universite Paris-Saclay, France), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract:

In live streaming applications, a fixed set of bitrate-resolution pairs (known as bitrate ladder) is generally used to avoid additional pre-processing run-time to analyze the complexity of every video content and determine the optimized bitrate ladder. Furthermore, live encoders use the fastest available preset for encoding to ensure the minimum possible latency in streaming. For live encoders, it is expected that the encoding speed is equal to the video framerate. However, an optimized encoding preset may result in (i) increased Quality of Experience (QoE) and (ii) improved CPU utilization while encoding. In this light, this paper introduces a Content-Adaptive encoder Preset prediction Scheme (CAPS) for adaptive live video streaming applications. In this scheme, the encoder preset is determined using Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features for every video segment, the number of
CPU threads allocated for each encoding instance, and the target encoding speed. Experimental results show that CAPS yields an overall quality improvement of 0.83 dB PSNR and 3.81 VMAF with the same bitrate, compared to the fastest preset encoding
of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder. This is achieved by maintaining the desired encoding speed and reducing CPU idle time.

Hadi

2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

September 26-28, 2022 | Shanghai, China

Conference Website

Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Prajit T Rajendran (Universite Paris-Saclay, Paris, France), Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),   Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK)and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract:

The increasing demand for high-quality and low-cost video streaming services calls for the prediction of video encoding complexity. The prior prediction of video encoding complexity including encoding time and bitrate predictions are used to allocate resources and set optimized parameters for video encoding effectively. In this paper, a light-weight video encoding complexity prediction (VECP) scheme that predicts the encoding bitrate and the encoding time of video with high accuracy is proposed. Firstly, low-complexity Discrete Cosine Transform (DCT)-energy-based features, namely spatial complexity, temporal complexity, and brightness of videos are extracted, which can efficiently
represent the encoding complexity of videos. The latent vectors are also extracted from a Convolutional Neural Network (CNN) with MobileNet as the backend to obtain additional features from representative frames of each video to assist the prediction process. The extreme gradient boosting (XGBoost) regression algorithm is deployed to predict video encoding complexity using the extracted features. The experimental results demonstrate that VECP predicts the encoding bitrate with an error percentage of up to 3.47% and encoding time with an error percentage of up to 2.89%, but with a significantly low overall latency of 3.5 milliseconds per frame which makes it suitable for both Video on Demand (VoD) and live streaming applications.

VECP architecture