ATHENA Christian Doppler (CD) Laboratory

ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator

*** 2nd Best Paper Award ***

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

Conference Website

[PDF][Slides][Video]

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.

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

07 December 2021  | Munich, Germany (Online)

[PDF][Slides][Video]

Daniele Lorenzi (University of Padua), Minh Nguyen (AAU, Austria), Farzad Tashtarian (AAU, Austria), Simone Milani (University of Padua), Herman Hellwagner (AAU, Austria),  Christian Timmerer (AAU, Austria)

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

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

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 in the blog post.

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.

Visual Communications and Image Processing (VCIP 2021)

5-8 December 2021, Munich, Germany

[PDF][Slides]

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.

Index Terms—HAS, per-title, deep learning, compression, bitrate ladder.