ATHENA Christian Doppler (CD) Laboratory

2022 NAB Broadcast Engineering and Information Technology (BEIT) Conference

April 24-26, 2022 | Las Vegas, US

[PDF][Slides]

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Feldmann (Bitmovin, Klagenfurt),
Adithyan Ilangovan (Bitmovin, Klagenfurt), Martin Smole (Bitmovin, Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).

Abstract:

Current per-title encoding schemes encode the same video content at various bitrates and spatial resolutions to find optimal bitrate-resolution pairs (known as bitrate ladder) for each video content in Video on Demand (VoD) applications. But in live streaming applications, a fixed bitrate ladder is used for simplicity and efficiency to avoid the additional latency to find the optimized bitrate-resolution pairs for every video content. However, an optimized bitrate ladder may result in (i) decreased storage or network resources or/and (ii) increased Quality of Experience (QoE). In this paper, a fast and efficient per-title encoding scheme (Live-PSTR) is proposed tailor-made for live Ultra High Definition (UHD) High Framerate (HFR) streaming. It includes a pre-processing step in which Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features are used to determine the complexity of each video segment, based on which the optimized encoding resolution and framerate for streaming at every target bitrate is determined. Experimental results show that, on average, Live-PSTR yields bitrate savings of 9.46% and 11.99% to maintain the same PSNR and VMAF scores, respectively compared to the HTTP Live Streaming (HLS) bitrate ladder.

Architecture of Live-PSTR

The Emmy® Awards do not only honour the work of actors and directors but also recognize technologies that are steadily improving the viewing experience for consumers.

This year, the winners include the MPEG DASH Standard. Christian Timmerer (Department of Information Technology) played a leading role in its development.

Read more about it here.

ACM Mile-High video Conference 2022 (MHV)

March 01-03, 2022 | Denver, CO, USA

[PDF][Slides][Video]

Minh Nguyen (AAU, Austria), Christian Timmerer (AAU, Austria), Stefan Pham (Fraunhofer FOKUS, Germany), Daniel Silhavy (Fraunhofer FOKUS, Germany), Ali C. Begen (Ozyegin University, Turkey)

Abstract: With the introduction of HTTP/3 (H3) and QUIC at its core, there is an expectation of significant improvements in Web-based secure object delivery. As HTTP is a central protocol to the current adaptive streaming methods in all major over-the-top (OTT) services, an important question is what H3 will bring to the table for such services. To answer this question, we present the new features of H3 and QUIC, and compare them to those of H/1.1/2 and TCP. We also share the latest research findings in this domain.

Keywords: HTTP adaptive streaming, QUIC, CDN, ABR, OTT, DASH, HLS.

ACM Mile-High video Conference 2022 (MHV)

March 01-03, 2022 | Denver, CO, USA

Conference Website

[PDF][Slides][Video]

Minh Nguyen (AAU, Austria), Ekrem Çetinkaya (AAU, Austria), Hermann Hellwagner (AAU, Austria), and Christian Timmerer (AAU, Austria)

Abstract: The advancement of mobile hardware in recent years made it possible to apply deep neural network (DNN) based approaches on mobile devices. This paper introduces a lightweight super-resolution (SR) network, namely SR-ABR Net, deployed at mobile devices to upgrade low-resolution/low-quality videos and a novel adaptive bitrate (ABR) algorithm, namely WISH-SR, that leverages SR networks at the client to improve the video quality depending on the client’s context. WISH-SR takes into account mobile device properties, video characteristics, and user preferences. Experimental results show that the proposed SR-ABR Net can improve the video quality compared to traditional SR approaches while running in real-time. Moreover, the proposed WISH-SR can significantly boost the visual quality of the delivered content while reducing both bandwidth consumption and the number of stalling events.

Keywords: Super-resolution, Deep Neural Networks, Mobile Devices, ABR

On Tuesday the 25^th of January 2022, Hadi Amirpour successfully defended his Ph.D. thesis under supervision of Assoc.-Prof. DI Dr. Christian Timmerer and Assoc.-Prof. Dr. Klaus Schöffmann. The defense was chaired by Assoc.-Prof. DI Dr. Mathias Lux and the examiners were Emeritus Prof. Dr. Mohammad Ghanbari (University of Essex, UK) and Univ.-Prof. DI Dr. Hermann Hellwagner (University of Klagenfurt).

We are pleased to congratulate Dr. Hadi Amirpour on passing his Ph.D. exam!

2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

May 22-27, 2022 | Singapore

[PDF][Slides]

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (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:

Current per-title encoding schemes encode the same video content at various bitrates and spatial resolutions to find an optimal bitrate ladder for each video content in Video on Demand (VoD) applications. However, in live streaming applications, a fixed resolution-bitrate ladder is used to avoid the additional encoding time complexity to find optimum resolution-bitrate pairs for every video content. This paper introduces an online per-title encoding scheme (OPTE) for live video streaming applications. In this scheme, each target bitrate’s optimal resolution is predicted from any pre-defined set of resolutions using Discrete Cosine Transform(DCT)-energy-based low-complexity spatial and temporal features for each video segment. Experimental results show that, on average, OPTE yields bitrate savings of 20.45% and 28.45% to maintain the same PSNR and VMAF, respectively, compared to a fixed bitrate ladder scheme (as adopted in current live streaming deployments) without any noticeable additional latency in streaming.

Keywords:

Per-title encoding, live streaming, bitrate ladder, convex-hull prediction

IEEE Transactions on Multimedia

[PDF]

Farzad Tashtarian (AAU, Austria), Abdelhak Bentaleb (NationalUniversity of Singapore), Alireza Erfanian (AAU, Austria), Hermann Hellwagner (AAU, Austria), Christian Timmerer (AAU, Austria), and Roger Zimmermann (National University of Singapore).

Abstract: While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers’ locations.

The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working.

Index Terms—Live streaming, HAS, DASH, HLS, CMAF, edge computing, low latency, caching, prefetching, transcoding.