Paper accepted – HxL3: Optimized Delivery Architecture for HTTP Low-Latency Live Streaming

IEEE Transactions on Multimedia

Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Abdelhak Bentaleb (National University of Singapore), Alireza Erfanian (Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), 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.

Paper accepted – LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming

IEEE International Conference on Communications (ICC)

May 16–20, 2022 | Seoul, South Korea

Conference Website

Reza Farahani (Alpen-Adria-Universität Klagenfurt),  Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt).

Abstract: With the emerging demands of high-definition and low-latency video streams, HTTP Adaptive Streaming (HAS) is considered the principal video delivery technology over the Internet. Network-assisted video streaming schemes, which employ modern networking paradigms, e.g., Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, have been introduced as promising complementary solutions in the HAS context to improve users’ Quality of Experience (QoE) as well as network utilization. However, the existing network-assisted HAS schemes have not fully used edge collaboration techniques and SDN capabilities for achieving the aforementioned aims. To bridge this gap, this paper introduces a coLlaborative Edge- and SDN-Assisted framework for HTTP aDaptive vidEo stReaming (LEADER). In LEADER, the SDN controller collects various information items and runs a central optimization model that minimizes the HAS clients’ serving time, subject to the network’s and edge servers’ resource constraints. Due to the NP-completeness and impractical overheads of the central optimization model, we propose an online distributed lightweight heuristic approach consisting of two phases that runs over the SDN controller and edge servers, respectively. We implement the proposed framework, conduct our experiments on a large-scale testbed including 250 HAS players, and compare its effectiveness with other strategies. The experimental results demonstrate that LEADER outperforms baseline schemes in terms of both users’ QoE and network utilization, by at least 22% and 13%, respectively.

Keywords:

Dynamic Adaptive Streaming over HTTP (DASH), Network-Assisted Video Streaming, Video Transcoding, Quality of Experience (QoE), Software-Defined Networking (SDN), Network Function Virtualization (NFV), Edge Computing, Edge Collaboration

Vignesh V Menon

Paper accepted – CODA: Content-aware Frame Dropping Algorithm for High Frame-rate Video Streaming

Data Compression Conference (DCC)

March 22-25, 2022 | Snowbird, Utah, US

Conference Website

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:

High Framerate (HFR) video streaming enhances the viewing experience and improves visual clarity. However, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, this paper proposes a content-aware frame dropping algorithm (CODA) to drop frames uniformly in every video (segment) according to the target bitrate and the video characteristics. The algorithm uses Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features to determine the video properties and then predict the optimized framerate, yielding the highest compression efficiency. The effectiveness of CODA is evaluated with High Efficiency Video Coding (HEVC) bitstreams based on the x265 HEVC open-source encoder. Experimental results show that, on average, CODA reduces the overall Ultra High Definition (UHD) encoding time by 21.82% with bit-rate savings of 15.87% and 18.20% to maintain the same PSNR and VMAF scores, respectively compared to the original frame-rate encoding.

Dragi Kimovski
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Paper accepted in the IEEE Computer Magazine: Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum

Title: Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum

Authors: Dragi Kimovski, Sasko Ristov, Radu Prodan

Abstract: The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research, or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.

5th Klagenfurt Winter Game Jam was a big success!

The 5th Klagenfurt Winter Game Jam took place as a hybrid event on Dec 17-19, 2021. Over 100 participants teamed up over the weekend to create altogether 26 games on the topic of “What’s in the box?”.

The games can be found and played at https://itch.io/jam/5th-winter-game-jam.

 

ARTICONF Project Meeting in Ohrid, Macedonia

In a hybrid (i.e. online and offline) attendance mode at the project meeting in Ohrid, Macedonia, the ARTICONF team gave a final push to have a unified and integrated ARTICONF toolset for DApp developers. The consortium led by project coordinator Prof. Prodan also outlined a detailed action plan for the remaining six months with regards to exploitation and dissemination of ARTICONF’s latest results and developed technologies.

 

Vignesh V Menon

VQEG NORM talk on Video Quality Analyzer

Vignesh V Menon and Hadi Amirpour gave a talk on ‘Video Complexity Analyzer for Streaming Applications’ at the Video Quality Experts Group (VQEG) meeting on December 14, 2021. Our research activities on video complexity analysis were presented in the talk.

The link to the presentation can be found here (pdf).

Prof. Radu Prodan
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Radu Prodan @ Horizon Cloud Summit 2021 & Cloud Expo Europe

The Horizon Cloud Summit 2021 – at its second edition – aims to gather innovators and researchers, Cloud adopters, policymakers, and Cloud initiatives and open source projects to shape the EU digital transition.

Radu Prodan held an online presentation: “ARTICONF: A Cloud-agnostic Blockchain-as-a-Service for Social Continuum“ on December 09, 2021.

 

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IEEE VCIP’21 Tutorial: A Journey towards Fully Immersive Media Access

IEEE VCIP 2021
Sunday, December 5, 2021
https://www.vcip2021.org/call-for-tutorials/

Find further info in the blog post here.

Prof. Radu Prodan
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Radu Prodan gave a keynote at ICAML 2021

Prof. Radu Prodan held a keynote speech about the ARTICONF project at the 3rd International Conference on Applications of AI & Machine Learning (ICAML 2021).