ATHENA Papers Accepted at ACM MMSys’21 Doctoral Symposium

ACM Multimedia Systems Conference (MMSys) 2021 | Doctoral Symposium

September 28 – October 01, 2021 | Istanbul, Turkey

Conference Website

Information about the individual papers can be found below.

CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming

Reza Farahani (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)


Abstract: Video streaming has become one of the most prevailing, bandwidth-hungry, and latency-sensitive Internet applications. HTTP Adaptive Streaming (HAS) has become the dominant video delivery mechanism over the Internet. Lack of coordination among the clients and lack of awareness of the network in pure client-based adaptive video bitrate approaches have caused problems, such as sub-optimal data throughput from Content Delivery Network (CDN) or origin servers, high CDN costs, and non-satisfactory users’ experience. Recent studies have shown that network-assisted HAS techniques by utilizing modern networking paradigms, e.g., Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing can significantly improve HAS system performance. In this doctoral study, we leverage the aforementioned modern networking paradigms and design network assistance for/by HAS clients to improve HAS systems performance and CDN/network utilization. We present four fundamental research questions to target different challenges in devising a network-assisted HAS system.

Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming

Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)


Abstract: Video traffic comprises the majority of today’s Internet traffic, and HTTP Adaptive Streaming (HAS) is the preferred method to deliver video content over the Internet. Increasing demand for video and the improvements in the video display conditions over the years caused an increase in the video coding complexity. This increased complexity brought the need for more efficient video streaming and coding solutions. The latest standard video codecs can reduce the size of the videos by using more efficient tools with higher time-complexities. The plans for integrating machine learning into upcoming video codecs raised the interest in applied machine learning for video coding. In this doctoral study, we aim to propose applied machine learning methods to video coding, focusing on HTTP adaptive streaming. We present four primary research questions to target different challenges in video coding for HTTP adaptive streaming.

Multi-access Edge Computing for Adaptive Bitrate Video Streaming

Jesús Aguilar-Armijo (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)


Abstract: Video streaming is the most used service in mobile networks and its usage will continue growing in the upcoming years. Due to this increase, content delivery should be improved as a key aspect of video streaming service, supporting higher bandwidth demand while assuring high quality of experience (QoE) for all the users. Multi-access edge computing (MEC) is an emerging paradigm that brings computational power and storage closer to the user. It is seen in the industry as a key technology for 5G mobile networks, with the goals of reducing latency, ensuring highly efficient network operation, improving service delivery and offering an improved user experience, among others. In this doctoral study, we aim to leverage the possibilities of MEC to improve the content delivery of video streaming services. We present four main research questions to target the different challenges in content delivery for HTTP Adaptive Streaming.

Optimizing QoE and Latency of Live Video Streaming Using Edge Computing and In-Network Intelligence

Alireza Erfanian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)


Abstract: Live video streaming traffic and related applications have experienced significant growth in recent years. More users have started generating and delivering live streams with high quality (e.g., 4K resolution) through popular online streaming platforms such as YouTube, Twitch, and Facebook. Typically, the video contents are generated by streamers and watched by many audiences, which are geographically distributed in various locations far away from the streamers’ locations. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users’ requested quality. In this thesis, we will investigate optimizing QoE and end-to-end (E2E) latency of live video streaming by leveraging edge computing capabilities and in-network intelligence. We present four main research questions aiming to address the various challenges in optimizing live streaming QoE and E2E latency by employing edge computing and in-network intelligence.

Policy-driven Dynamic HTTP Adaptive Streaming Player Environment

Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)


Abstract: Video streaming services account for the majority of today’s traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user’s environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today’s multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user’s needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).

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LwTE: Light-weight Transcoding at the Edge

LwTE: Light-weight Transcoding at the Edge


Alireza Erfanian* (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hadi Amirpour*, (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt),  Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

*These authors contributed equally to this work.


Abstract: Due to the growing demand for video streaming services, providers have to deal with increasing resourcerequirements for increasingly heterogeneous environments. To mitigate this problem, many works have beenproposed which aim to (i) improve cloud/edge caching efficiency, (ii) use computation power available in thecloud/edge for on-the-fly transcoding, and (iii) optimize the trade-off among various cost parameters,e.g.,storage, computation, and bandwidth. In this paper, we proposeLwTE, a novelLight-weightTranscodingapproach at theEdge, in the context of HTTP Adaptive Streaming (HAS). During the encoding processof a video segment at the origin side, computationally intense search processes are going on. The mainidea ofLwTEis to store the optimal results of these search processes as metadata for each video bitrateand reuse them at the edge servers to reduce the required time and computational resources for on-the-fly transcoding.LwTEenables us to store only the highest bitrate plus corresponding metadata (of verysmall size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction inbandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment isremarkably decreased by utilizing its corresponding metadata; unnecessary search processes are avoided.Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD)streaming services by optimizing storage and computation (transcoding) costs at the edge servers and thencompare it to conventional methods (store all bitrates, partial transcoding). The results indicate that ourapproach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to70% compared to the state-of-the-art approaches.

Keywords: Video streaming, transcoding, video on demand, edge computing.

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WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)

October 06-08 | Tampere, Finland

Conference Website


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

Abstract: Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.

Keywords: ABR Algorithms, HTTP Adaptive Streaming, ITU-T P.1203, WISH

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INCEPT: INTRA CU Depth Prediction for HEVC

 IEEE 23rd International Workshop on Multimedia Signal Processing

October 06–08, 2021, Tampere, Finland

Conference Website


Vignesh V Menon (Alpen-Adria-Universitat Klagenfurt); Hadi Amirpour (Alpen-Adria-Universität Klagenfurt); Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria); Mohammad Ghanbari (University of Essex, UK).


High Efficiency Video Coding (HEVC) improves the encoding efficiency by utilizing sophisticated tools such as flexible Coding Tree Unit (CTU) partitioning. The Coding Unit (CU) can be split recursively into four equally sized CUs ranging from 64×64 to 8×8 pixels. At each depth level (or CU size), intra prediction via exhaustive mode search was exploited in HEVC to improve the encoding efficiency and result in a very high encoding time complexity. This paper proposes an Intra CU Depth Prediction (INCEPT) algorithm, which limits Rate-Distortion Optimization (RDO) for each CTU in HEVC by utilizing the spatial correlation with the neighboring CTUs, which is computed using a DCT energy-based feature. Thus, INCEPT reduces the number of candidate CU sizes required to be considered for each CTU in HEVC intra coding. Experimental results show that the INCEPT algorithm achieves a better trade-off between the encoding efficiency and encoding time saving (i.e., BDR/∆T) than the benchmark algorithms. While BDR/∆T is 12.35% and 9.03% for the benchmark algorithms, it is 5.49% for the proposed algorithm. As a result, INCEPT achieves a 23.34% reduction in encoding time on average while incurring only a 1.67% increase in bit rate than the original coding in the x265 HEVC open-source encoder.

Keywords: HEVC, Intra coding, CTU, CU, depth decision

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Farzad Tashtarian and Christian Timmerer are Co-Chairs of ViSNext 2021 Workshop at the ACM CoNEXT 2021 Conference

ViSNext’21: 1st ACM CoNEXT Workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming

In recent years, we have witnessed phenomenal growth in live video traffic over the Internet, accelerated by the rise of novel video streaming technologies, advancements in networking paradigms, and our ability to generate, process, and display videos on heterogeneous devices. Regarding the existing constraints and limitations in different components on the video delivery path from the origin server to clients, the network plays an essential role in boosting the perceived Quality of Experience (QoE) by clients. The ViSNext workshop aims to bring together researchers and developers working on all aspects of video streaming, in particular network-assisted concepts backed up by experimental evidence. We warmly invite submission of original, previously unpublished papers addressing key issues in this area, but not limited to:

  • Design, analysis, and evaluation of network-assisted multimedia system architectures
  • Optimization of edge, fog, and mobile edge computing for video streaming applications
  • Optimization of caching policies/systems for video streaming applications
  • Network-assisted resource allocation for video streaming
  • Experience and lessons learned by deploying large-scale network-assisted video streaming
  • Internet measurement and modeling for enhancing QoE in video streaming applications
  • Design, analysis, and evaluation of network-assisted Adaptive Bitrate (ABR) streaming
  • Network aspects in video streaming: cloud computing, virtualization techniques, network control, and management, including SDN, NFV, and network programmability
  • Routing and traffic engineering in end-to-end video streaming
  • Topics at the intersection of energy-efficient computing and networking for video streaming
  • Network-assisted techniques for low-latency video streaming
  • Machine learning for improving QoE in video streaming applications
  • Machine learning for traffic engineering and congestion control for video streaming
  • Solutions for improving streaming QoE for high-speed user mobility
  • Analysis, modeling, and experimentation of DASH
  • Big data analytics at the network edge to assess viewer experience of adaptive video
  • Reproducible research in adaptive video streaming: datasets, evaluation methods, benchmarking, standardization efforts, open-source tools
  • Novel use cases and applications in the area of adaptive video streaming
  • Advanced network-based techniques for point clouds, light field, and immersive video
  • Low delay and multipath video communication

For further information please click here.

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EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming

The 46th IEEE Conference on Local Computer Networks (LCN)

Conference Website


Jesús Aguilar Armijo (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt) and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt)

Abstract: Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE).

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Throwback on our special session at PCS’21

A Special Session on ‘Video Coding for Large Scale HTTP Adaptive Streaming Deployments‘ was organized by Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Ghanbari (University of Essex, UK), and Alex Giladi (Comcast, USA) on July 2 at the 35th Picture Coding Symposium (PCS) 2021.

Four papers were presented during this session as shown below:

1. VMAF-based Bitrate Ladder Estimation for Adaptive Streaming

Authors: Angeliki Katsenou (University of Bristol); Fan Zhang (University of Bristol); Kyle Swanson (Netflix); Mariana Afonso (Netflix); Joel Sole (Netflix); David Bull (University of Bristol)

Abstract: In HTTP Adaptive Streaming, video content is conventionally encoded by adapting its spatial resolution and quantization level to best match the prevailing network state and display characteristics. It is well known that the traditional solution, of using a fixed bitrate ladder, does not result in the highest quality of experience for the user. Hence, in this paper, we introduce a content-driven approach for estimating the bitrate ladder, based on spatio-temporal features extracted from the uncompressed content. The method implements a content-driven interpolation. It uses the extracted features to train a
machine learning model to infer the curvature points of the Rate-VMAF curves in order to guide a set of initial encodings. We employ the VMAF quality metric as a means of perceptually conditioning the estimation. When compared to the generation of a reference ladder using exhaustive encoding, 76.63% the estimated ladder’s Rate-VMAF points are identical to those of the reference ladder. The proposed method benefits from a significant
(77.4%) reduction in the number of encodes required with only a small (1.04%) average Bjøntegaard Delta Rate increase.

2. Efficient Multi-Encoding Algorithms for HTTP Adaptive Bitrate Streaming

Authors: Vignesh V Menon (Alpen-Adria-Universitat Klagenfurt); Hadi Amirpour (Alpen-Adria-Universität Klagenfurt); Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria); Mohammad Ghanbari (University of Essex, UK)

Abstract: Since video accounts for the majority of today’s internet traffic, the popularity of HTTP Adaptive Streaming (HAS) is increasing steadily. In HAS, each video is encoded at multiple bitrates and spatial resolutions (i.e., representations) to adapt to a heterogeneity of network conditions, device characteristics, and end-user preferences. Most of the streaming services utilize cloud-based encoding techniques which enable a fully parallel encoding process to speed up the encoding and consequently to reduce the overall time complexity. State-of-the-art approaches further improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to improve the encoding time complexity of the remaining representations. In this paper, we investigate various multi-encoding algorithms (i.e., multi-rate and multi-resolution) and propose novel multi-encoding algorithms for large-scale HTTP Adaptive Streaming deployments. Experimental results demonstrate that the proposed multi-encoding algorithm optimized for the highest compression efficiency reduces the overall encoding time by 39% with a 1.5% bitrate increase compared to stand-alone encodings. Its optimized version for the highest time savings reduces the overall encoding time by 50% with a 2.6% bitrate increase compared to standalone encodings.

More details on x265 can be found here.

3. Open GOP Resolution Switching in HTTP Adaptive Streaming with VVC

Authors: Robert Skupin (Fraunhofer HHI); Christian Bartnik (Fraunhofer HHI); Adam Wieckowski (HHI); Yago Sanchez de la Fuente (Fraunhofer HHI); Benjamin Bross (HHI); Cornelius Hellge (Fraunhofer HHI); Thomas Schierl (Fraunhofer HHI)

Abstract: The user experience in adaptive HTTP streaming relies on offering bitrate ladders with suitable operation points for all users and typically involves multiple resolutions. While open GOP coding structures are generally known to provide
substantial coding efficiency benefit, their use in HTTP streaming has been precluded through lacking support of reference picture resampling (RPR) in AVC and HEVC. The
newly emerging Versatile Video Coding (VVC) standard supports RPR, but only conversational scenarios were primarily investigated during the design of VVC. This paper aims at enabling usage of RPR in HTTP streaming scenarios through analysing the drift potential of VVC coding tools and presenting a constrained encoding method that avoids severe drift artefacts in resolution switching with open GOP coding in VVC. In
typical live streaming configurations, the presented method achieves -8.7% BD-rate reduction compared to closed GOP coding while in a typical Video on Demand configuration, -1.89% BD-rate reduction is reported. The constraints penalty
compared to regular open GOP coding is 0.65% BD-rate in the worst case. The presented method was integrated into the publicly available open source VVC encoder VVenC v0.3.

The source code of VVenc can be accessed here. The source code of the VVC reference software  (VTM) can be accessed here.

4. Towards Understanding of the Behavior of Web Streaming

Authors: Yuriy Reznik (Brightcove, Inc.); Karl Lillevold (Brightcove, Inc.); Abhijith Jagannath (Brightcove, Inc.); Xiangbo Li (Brightcove, Inc.

Abstract: We study the behavior of a modern-era adaptive streaming system delivering videos embedded in web-pages. In such an application, the size of videos rendered on the screen may depend on user preferences, such as the position and size of a browser window. Moreover, the stream selection logic in such a system appears to be influenced not only by the available network bandwidth but also by the output video size, which, in many cases, limits the selection of higher quality streams. To explain this behavior, in this paper we introduce a simple analytical model of a client adapting to both bandwidth and player size. Using this model, we then compute stream selection probabilities and show
that they are sufficiently close to respective statistics observed in practical experiments. Possible uses of this proposed client model are also suggested. Specifically, we show how it can be used to derive formulae for the average performance parameters of the system and also for posing related optimization problems.

The dataset as mentioned during the presentation can be accessed here.

We thank the organizers of PCS’21, the authors, and reviewers of the presented papers, the attendees who participated with probing questions, making the session successful.

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A Distributed Delivery Architecture for User Generated Content Live Streaming over HTTP

The 46th IEEE Conference on Local Computer Networks (LCN)

Conference Website


Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Abdelhak Bentaleb (National University of Singapore), Reza Farahani (Alpen-Adria-Universität Klagenfurt), Minh Nguyen (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt), and Roger Zimmermann (National University of Singapore)

Abstract: Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGCsystems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA(joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers(ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an optimal solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance ofQuaLAin achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutions

Keywords: UGC streaming, low latency live streaming, fair-ness, QoE, HAS, DASH, ABR, adaptive streaming, ADMM.

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CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming

The 46th IEEE Conference on Local Computer Networks (LCN)

Conference Website


Reza Farahani (Alpen-Adria-Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Hadi Amirpour (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 increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. In this paper, we leverage the capability of Software-Define Networking (SDN), Network Function Virtualization (NFV), and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming framework called CSDN. We employ virtualized edge entities to collect various information items (e.g., user-, client, CDN- and network-level information) in a time-slotted method. These components then run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers (in terms of fetch and transcoding times). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, (ii) by a quality transcoded from an optimal replacement quality at the edge, or (iii) by the originally requested quality level from the origin server. By means of comprehensive experiments conducted on a real-world large-scale testbed, we demonstrate that CSDN outperforms the state-of-the-art in terms of playback bitrate, the number of quality switches, the number of stalls, and bandwidth usage by at least 7.5%, 19%, 19%, and 63%, respectively.

Keywords: Dynamic Adaptive Streaming over HTTP (DASH), Edge Computing, Network-Assisted Video Streaming, Quality of Experience (QoE), Software Defined Networking (SDN), Network Function Virtualization (NFV), Video Transcoding, Content Delivery Network (CDN).

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Christian Doppler laboratory ATHENA celebrates successful evaluation of its first research phase

The Christian Doppler laboratory ATHENA (AdapTive Streaming over HTTP and Emerging Networked MultimediA Services) has been jointly proposed by the Institute of Information Technology (ITEC; at Alpen-Adria-Universität Klagenfurt (AAU; and Bitmovin GmbH ( to address current and future research and deployment challenges of HTTP Adaptive Streaming (HAS) and emerging streaming methods. The aim of ATHENA is to research and develop novel paradigms, approaches, (prototype) tools, and evaluation results for the phases (1) multimedia content provisioning (WP-1), (2) content delivery (WP-2), and (3) content consumption (WP-3) in the media delivery chain as well as for (4) end-to-end aspects (WP-4), with a focus on, but not being limited to, HTTP Adaptive Streaming (HAS).

In this blog post, I’d like to briefly review how it started until the official review of the first phase in April 2021 that took place online. The result of this review is provided at the end.

ATHENA started in October 2019 with an international team of professors, Post-docs, Ph.D. students, and administrative personnel after an intensive hiring period from approx. February to December 2019. Additionally, we had to establish an entirely new office space from scratch (incl. planning thereof) as part of the Educational Lab of the Lakeside Science and Technology Park (due to space limitations of ITEC/AAU). Finally, the official opening event was celebrated as part of the 50-year celebration of AAU on January 21, 2020. Shortly after starting ATHENA, the COVID-19 pandemic reached also Austria and, thus, ATHENA, which significantly impacted our daily lives and work routines. The ATHENA team was required to switch to work-from-home (WFH) within one day including the management thereof, without prior experience in managing remote teams/individuals. However, ATHENA swiftly managed this transition thanks to ITEC administrative staff and Bitmovin who provided administrative support.

Despite all the issues caused by COVID-19, ATHENA was able to manage this extreme situation in a professional way leading to a number of publications in renowned publication venues. ATHENA was accepted as a pilot laboratory with the requirement to conduct application-oriented basic research according to the funding model of the Christian Doppler Gesellschaft (CDG). In this context, the ATHENA research resulted in various advances in adaptive streaming over HTTP and emerging networked multimedia services as outlined in the scientific part of this report. In particular, three journal publications with high impact/quality factor I and 17 conference/workshop publications with high quality factor I-II according to AAU’s internal ranking guidelines based on internationally recognized rankings (e.g., CORE). Furthermore, ATHENA contributed to a white paper on Definitions of Immersive Media Experience. Finally, various publications are currently in progress, i.e., under review/revision or to be submitted soon. The latest information about all ATHENA publications can be found here  (25 at the time of writing this blog post).

Christian Doppler laboratories are scientifically peer-reviewed regularly in between different phases of the project. The first review is typically done before the completion of the second year, and, thus, ATHENA has been reviewed in April 2021 (i.e., a bit earlier due to its ‘pilot’ status) based on the review report submitted in March 2021. This report included an overview of scientific contributions made during the first phase as well as a detailed research plan for the second phase of ATHENA. The actual review meeting was organized as an online meeting (due to COVID-19) comprising several administrative/overview presentations but also four technical presentations:

  • Video Coding for HTTP Adaptive Streaming: State of the Art, Recent Advances, and Future Perspectives
  • Network Assistance for HTTP Adaptive Streaming
  • H2BR: HTTP/2-based Segment Upgrading to Improve the Quality of Experience in HTTP Adaptive Streaming
  • QoE-Fairness in Low Latency Live Streaming

The Christian Doppler Research Association discussed the outcome of this ATHENA review in their meeting in June 2021. It is my pleasure to announce that the ATHENA CD ‘pilot’ lab is transferred into a regular CD lab starting October 2021 (i.e., received a successful, positive evaluation report) and that the budget for the second phase has been approved (i.e., Oct’21-Sep’24). I’d like to thank the CDG and the external reviewer for their valuable support and constructive feedback during this phase of ATHENA and I’m looking forward to an exciting and innovative second phase of the project. Finally, I’d like to thank the Alpen-Adria-Universität Klagenfurt, our industrial partner Bitmovin, and our ATHENA team for their tremendous efforts and contributions towards this first successful milestone. Stay tuned…

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