ATHENA Christian Doppler (CD) Laboratory

The 1st ACM CoNEXT Workshop on Design, Deployment, and Evaluation of Network-assisted  video Streaming (ViSNext 2021)

Conference Website

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Alireza Erfanian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increase the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the band-width utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata, and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.

Keywords: live video streaming, network function virtualization, NFV, light-weight transcoding, transcoding, edge computing

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.