ATHENA Christian Doppler (CD) Laboratory

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

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.

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.

28th International Conference on Multimedia Modeling (MMM)

April 05-08, 2022 | Phu Quoc, Vietnam

Conference Website

[PDF][Poster]

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.

28th International Conference on Multimedia Modeling (MMM)

April 05-08, 2022 | Phu Quoc, Vietnam

Conference Website

[PDF]

Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), and Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: Deep neural network (DNN) based approaches have been intensively studied to improve video quality thanks to their fast advancement in recent years. These approaches are designed mainly for desktop devices due to their high computational cost. However, with the increasing performance of mobile devices in recent years, it became possible to execute DNN based approaches in mobile devices. Despite having the required computational power, utilizing DNNs to improve the video quality for mobile devices is still an active research area. In this paper, we propose an open-source mobile platform, namely MoViDNN, to evaluate DNN based video quality enhancement methods, such as super-resolution, denoising, and deblocking. Our proposed platform can be used to evaluate the DNN based approaches both objectively and subjectively. For objective evaluation, we report common metrics such as execution time, PSNR, and SSIM. For subjective evaluation, Mean Score Opinion (MOS) is reported. The proposed platform is available publicly at https://github.com/cd-athena/MoViDNN

Keywords: Super resolution, Deblocking, Deep Neural Networks, Mobile Devices