Multimedia Communication

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Paper accepted: ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator

The 10th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)

Conference Website

Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: In this work, we present ANGELA, HTTP adaptive streaming (HAS) and Edge Computing Simulator. ANGELA was designed to test edge mechanisms that support HAS, as it offers: realistic radio layer simulation, different multimedia content configurations, access to radio and player metrics at the edge, and a wide variety of metrics to evaluate the video streaming session performance. The ANGELA architecture is flexible and can support adaptive bitrate (ABR) algorithms located at different points of the network. Moreover, we show the possibilities of Angela by evaluating different ABR algorithms.

Keywords: Network simulator, testbed, edge computing, HTTP Adaptive Streaming.

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Paper accepted: Days of Future Past: An Optimization-based Adaptive Bitrate Algorithm over HTTP/3

The ACM CoNEXT 2021 Workshop on the Evolution, Performance, and Interoperability of QUIC (EPIQ)

07 December 2021  | Munich, Germany (Online)

Workshop Website

Daniele Lorenzi (Department of Information Engineering, University of PaduaItaly), Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Simone Milani (Department of Information Engineering, University of PaduaItaly), Herman Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt),  Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: HTTP Adaptive Streaming(HAS) has become a predominant technique for delivering videos in the Internet. Due to its adaptive behaviour according to changing network conditions it may result in video quality variations that negatively impacts the Quality of Experience (QoE) of the user. In this paper, we propose Days of Future Past, an optimization-based Adaptive Bitrate (ABR) algorithm over HTTP/3. Days of Future Past takes advantage of an optimization model and HTTP/3 features, including (i) stream multiplexing, and (ii) request cancellation. We design a Mixed Integer Linear Programming (MILP) model that determines the optimal video qualities of both next segment requests and the segments currently located in the buffer. If better qualities for buffered segments are found, the client will send corresponding HTTP GET requests to retrieve them. Multiple segments (i.e., re-transmitted segments) might be downloaded simultaneously to upgrade some buffered but not yet played segments to avoid quality decreases using the stream multiplexing feature of QUIC. HTTP/3’s request cancellation will be used in case retransmitted segments will arrive at the client after their playout time. The experimental results shows that our proposed method is able to improve the QoE by up to 33.9 %.

Keywords: HTTP/3, QUIC, Days of Future Past, HAS, QoE

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Farzad Tashtarian to give a talk at the University of Isfahan, Isfahan, Iran

Farzad Tashtarian is invited to talk on “Network-Assisted Video Streaming” at the University of Isfahan, Isfahan, Iran.

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Best Doctoral Symposium Paper Award at ACM MMSys 2021

Ekrem Çetinkaya got the Best Doctoral Symposium Paper Award at ACM MMSys 2021 for his paper titled “Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming”. More information about the paper can be found HERE.

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FaRes-ML paper is Nominated for the Best New Streaming Innovation Award in the Streaming Media Readers’ Choice Awards 2021

The Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning paper from ATHENA lab is nominated for the Best New Streaming Innovation Award in the Streaming Media Readers’ Choice Awards 2021.

Voting can be done on the awards’ website. The voting is open until October 4. You can find the paper under the Best New Streaming Innovation Award section as following:

More information about the paper can be found here.

Hadi
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Paper accepted – On The Impact of Viewing Distance on Perceived Video Quality

Title: On The Impact of Viewing Distance on Perceived Video Quality

Link: IEEE Visual Communications and Image Processing (VCIP 2021) 5-8 December 2021, Munich, Germany

Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Raimund Schatz (AIT Austrian Institute of Technology, Austria), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: Due to the growing importance of optimizing quality and efficiency of video streaming delivery, accurate assessment of user perceived video quality becomes increasingly relevant. However, due to the wide range of viewing distances encountered in real-world viewing settings, actually perceived video quality can vary significantly in everyday viewing situations. In this paper, we investigate and quantify the influence of viewing distance on perceived video quality.  A subjective experiment was conducted with full HD sequences at three different stationary viewing distances, with each video sequence being encoded at three different quality levels. Our study results confirm that the viewing distance has a significant influence on the quality assessment. In particular, they show that an increased viewing distance generally leads to an increased perceived video quality, especially at low media encoding quality levels. In this context, we also provide an estimation of potential bitrate savings that knowledge of actual viewing distance would enable in practice.
Since current objective video quality metrics do not systematically take into account viewing distance, we also analyze and quantify the influence of viewing distance on the correlation between objective and subjective metrics. Our results confirm the need for distance-aware objective metrics when accurate prediction of perceived video quality in real-world environments is required.

Hadi
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Paper accepted – Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution

Title: Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution

Link: IEEE Visual Communications and Image Processing (VCIP 2021) 5-8 December 2021, Munich, Germany

Authors: Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Hannaneh Barahouei Pasandi (Virginia Commonwealth University), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)

Abstract: In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional approaches and they are becoming more and more efficient over time.  This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms as they show a significant gain over traditional approaches. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates a maximum threshold for the quality is set and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method.

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Paper accepted – INTENSE: In-depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in HTTP Adaptive Streaming

Title: INTENSE: In-depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in HTTP Adaptive Streaming

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

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Babak Taraghi (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: With the recent growth of multimedia traffic over the Internet and emerging multimedia streaming service providers, improving Quality of Experience (QoE) for HTTP Adaptive Streaming (HAS) becomes more important. Alongside other factors, such as the media quality, HAS relies on the performance of the media player’s Adaptive Bitrate (ABR) algorithm to optimize QoE in multimedia streaming sessions. QoE in HAS suffers from weak or unstable internet connections and suboptimal ABR decisions. As a result of imperfect adaptiveness to the characteristics and conditions of the internet connection, stall events and quality level switches could occur and with different durations that negatively affect the QoE. In this paper, we address various identified open issues related to the QoE for HAS, notably (i) the minimum noticeable duration for stall events in HAS;(ii) the correlation between the media quality and the impact of stall events on QoE; (iii) the end-user preference regarding multiple shorter stall events versus a single longer stall event; and (iv) the end-user preference of media quality switches over stall events. Therefore, we have studied these open issues from both objective and subjective evaluation perspectives and presented the correlation between the two types of evaluations. The findings documented in this paper can be used as a baseline for improving ABR algorithms and policies in HAS.

Keywords: Crowdsourcing; HTTP Adaptive Streaming; Quality of Experience; Quality Switches; Stall Events; Subjective Evaluation; Objective Evaluation.

Interns at ATHENA project (Summer 2021)

In July 2021, the ATHENA Christian Doppler Laboratory hosted three interns working on the following topics:

  • Kassian Fuger: Machine Learning and Video Encoding for HTTP Adaptive Streaming
  • Timo Pfeifer: HTTP Adaptive Streaming for Mobile Devices
  • Vanessa Fröhlich: Machine Learning and its Applications

At the end of the internship, the interns presented their work and the results in the form of a presentation, and they have obtained a certificate of internship. We believe that the joint work was useful both for the laboratory and for the interns themselves. We would like to thank the interns for their genuine interest, productive work,  and excellent feedback about our laboratory.

Kassian Fuger: “Those four weeks of the internship meant a lot for my future career path. The internship gave me a great view of what working with a smart and friendly team is like. Working on my tasks by myself taught me a lot and I learned and improved in every aspect of the internship and if something was unclear my supervisor was always there to help. I was well accepted into the team and we sometimes went for lunch together which was very nice and helped me to be confident around the team. Anyways spending a part of my summertime at ATHENA was super fun and good time investment. Being on the ATHENA team was a great experience and I think everyone can learn something there.”

Vanessa Fröhlich: “I really enjoyed the internship at ATHENA and would even be happy to do it a second time. It was a great experience working with the people there and getting to know certain things that I would have definitely not learned elsewhere. It was very interesting to work with a new type of programming! I also appreciated very much how I felt part of the team in the first days already and also enjoyed working independently but with the help of my supervisor Farzad whenever I needed something or was stuck with an exercise. I am very glad that I found this internship and can say that I have gained a lot of experience there. Thank you!”

We wish the interns every success in their journey through life and we hope to see them soon back at the University of Klagenfurt and ATHENA.

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Special issue on Open Media Compression: Overview, Design Criteria, and Outlook on Emerging Standards

Special issue on Open Media Compression: Overview, Design Criteria, and Outlook on Emerging Standards

Proceedings of the IEEE, vol. 109, no. 9, Sept. 2021

By CHRISTIAN TIMMERER, Senior Member IEEE
Guest Editor
MATHIAS WIEN, Member IEEE
Guest Editor
LU YU, Senior Member IEEE
Guest Editor
AMY REIBMAN, Fellow IEEE Guest Editor

Abstract: Multimedia content (i.e., video, image, audio) is responsible for the majority of today’s Internet traffic and numbers are expecting to grow beyond 80% in the near future. For more than 30 years, international standards provide tools for interoperability and are both source and sink for challenging research activities in the domain of multimedia compression and system technologies. The goal of this special issue is to review those standards and focus on (i) the technology developed in the context of these standards and (ii) research questions addressing aspects of these standards which are left open for competition by both academia and industry.

Index Terms—Open Media Standards, MPEG, JPEG, JVET, AOM, Computational Complexity

C. Timmerer, M. Wien, L. Yu and A. Reibman, “Special issue on Open Media Compression: Overview, Design Criteria, and Outlook on Emerging Standards,” in Proceedings of the IEEE, vol. 109, no. 9, pp. 1423-1434, Sept. 2021, doi: 10.1109/JPROC.2021.3098048.

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