Multimedia Communication

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.

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).

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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.

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ANGELA won the 2nd Best Paper Award in IFIP/IEEE PEMWN 2021 Conference

The ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator paper from ATHENA CD laboratory has won the 2nd Best Paper Award in the 10th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN).

More information about the paper can be found in the blog post.

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Farzad Tashtarian to give a talk at IMDEA Networks Institute, Madrid, Spain

Farzad Tashtarian is invited to talk on “LwTE: Light-weight Transcoding at the Edge” at IMDEA Networks Institute, Madrid, Spain.

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Internship 2022 at ATHENA

You are a Master Student and want to get to know more about ATHENA in a 3 months ATHENA internship in 2022?

Come and join our team! Apply now.

(Please note: application deadline is 14 December 2021)

 

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FaRes-ML Won 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 has won the Best New Streaming Innovation Award in the Streaming Media Readers’ Choice Awards 2021.

The journey that led to the publication of the FaRes-ML paper was quite an insightful one.

It all started with the question, “How to efficiently provide multi-rate representations over a wide range of resolutions for HTTP Adaptive Streaming?“. This led to the first publication, Fast Multi-Rate Encoding for Adaptive HTTP Streaming, in which we proposed a double-bound approach to speed up the multi-rate encoding. After analyzing the results, we saw room for improvement in parallel encoding performance, which led to the second publication Towards Optimal Multirate Encoding for HTTP Adaptive Streaming. The results were promising, but we believed we could improve the encoding performance by utilizing machine learning. That was the primary motivation behind our third paper, FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning. In FaMe-ML, we have used convolutional neural networks (CNNs) to use the information from the reference representation better to encode other representations, resulting in significant improvement in the multi-rate encoding performance. Finally, we proposed FaRes-ML to extend our FaME-ML approach to include multi-resolution scenarios in Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning paper.

Here is the list of publications that led to FaRes-ML:

  1. Fast Multi-Rate Encoding for Adaptive HTTP Streaming. Published in DCC’20.
  2. Towards Optimal Multirate Encoding for HTTP Adaptive Streaming. Published in MMM’21.
  3. FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning. Published in VCIP’20.
  4. Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning. Published in IEEE OJ-SP.
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Christian Timmerer and Babak Taraghi will give talks at DDRC’21

Taichung, Taiwan, The 1st IEEE International Workshop on Data-Driven Rate Control for Media Streaming (DDRC’21) Co-located with the IEEE International Conference on Multimedia Big Data (BigMM’21)

Conference Website: https://www.bigmm.org/ (November 15-17)

HTTP Adaptive Streaming (HAS) — Quo Vadis?
Speaker: Professor Christian Timmerer
Time: November 16, 2021 12:10 (UTC +1)

CAdViSE or how to find the Sweet Spots of ABR Systems
Speaker: Babak Taraghi, M.Sc.
Time: November 16, 2021 13:00 (UTC +1)

Online attendance is free, Visit here for more information.

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Paper accepted: ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming

28th International Conference on Multimedia Modeling (MMM)

April 05-08, 2022 | Qui Nhon, Vietnam

Conference Website

Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive
bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques
to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.

Keywords: HTTP Adaptive Streaming, Edge Computing, Content Delivery, Network-assisted Video Streaming, Quality of Experience, Machine Learning.