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Paper accepted: DIGITWISE: Digital Twin-based Modeling of Adaptive Video Streaming Engagement

ACM MMSys 2024, Bari, Italy, Apr. 15-18, 2024 

Authors: Emanuele Artioli (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: As the popularity of video streaming entertainment continues to grow, understanding how users engage with the content and react to its changes becomes a critical success factor for every stakeholder. User engagement, i.e., the percentage of video the user watches before quitting, is central to customer loyalty, content personalization, ad relevance, and A/B testing. This paper presents DIGITWISE, a digital twin-based approach for modeling adaptive video streaming engagement. Traditional adaptive bitrate (ABR) algorithms assume that all users react similarly to video streaming artifacts and network issues, neglecting individual user sensitivities. DIGITWISE leverages the concept of a digital twin, a digital replica of a physical entity, to model user engagement based on past viewing sessions. The digital twin receives input about streaming events and utilizes supervised machine learning to predict user engagement for a given session. The system model consists of a data processing pipeline, machine learning models acting as digital twins, and a unified model to predict engagement. DIGITWISE employs the XGBoost model in both digital twins and unified models. The proposed architecture demonstrates the importance of personal user sensitivities, reducing user engagement prediction error by up to 5.8% compared to non-user-aware models. Furthermore, DIGITWISE can optimize content provisioning and delivery by identifying the features that maximize engagement, providing an average engagement increase of up to 8.6 %.

Keywords: digital twin, user engagement, xgboost

 

 

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Webinar of Graph-Massivizer @ HiPEAC

Funded by the EU, HiPEAC (High-Performance Edge And Cloud computing) is the premier focal point for networking, dissemination, training, and collaboration activities in Europe for researchers, industry, and policy related to computing systems.

The HiPEAC webinar series allows you to keep up to date on the latest advances in computer architecture and compilation research via online sessions, which can be accessed anywhere.

The Graph-Massivizer Project webinar took place on November 29, 2023. After an introduction by Nuria De Lama,  Radu Prodan presented the background of GraphProcessing, from Euler‘s five-node graphs to the massive graphs of today, and the motivations for the project.

 Project details have been presented by Reza Farahani and Matteo Angelinelli.

WEBINAR is online now: https://www.youtube.com/watch?v=YW7pD6nPMhs

 

 

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Successful Collaboration with Bitmovin Team in APAC

With the current popularity of ECO in the Asia–Pacific (APAC), the Bitmovin team in APAC, led by Adrian Britton, expressed an interest in the energy-aware research initiatives conducted within the GAIA project in Austria. Following an introductory meeting between the APAC team and AAU on October 17, 2023, both teams decided to meet in person on November 21, 2023, to explore the topics further.

The meeting proved to be highly productive, centering around two recent research topics:

– VE-Match: Video Encoding Matching-Based Model in the Cloud and Edge (presented by Samira Afzal & Narges Mehran)

– Energy-aware Spatial and Temporal Resolution Selection for Per-Title (presented by Mohammad Ghasempour & Hadi Amirpour)

Many interesting Q&As appeared during each presentation due to customer and provider requirements and the future insight of climate-friendly video streaming in the Cloud and Edge. The fruitful discussions opened up avenues for future exploration in this dynamic field.

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Paper accepted: Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality @ CoNEXT, Paris December 5-8 2023

The 19th International Conference on emerging Networking EXperiments and Technologies (CoNEXT) Paris, France, December 5-8, 2023

Authors: Leonardo Peroni (IMDEA Networks Institute), Sergey Gorinsky (IMDEA Networks Institute), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria).

Abstract: Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85\% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.

 

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Paper accepted: Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Streaming @ IEEE VCIP´23

International Conference on Visual Communications and Image Processing (IEEE VCIP’23)

http://www.vcip2023.org/

Authors: Vignesh V Menon, Reza Farahani, Prajit T Rajendran, Samira Afzal, Klaus Schoeffmann, Christian Timmerer

Abstract: With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission. To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., HEVC, AV1) that lie below the predicted rate-distortion curve of the AVC codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.

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Paper accepted: Community-Based QoE Enhancement for User-Generated Content Live Streaming @ 13th International Conference on Computer and Knowledge Engineering (ICCKE)

Authors: Reza Saeedinia (University of Tehran), S. Omid Fatemi (University of Tehran),Daniele Lorenzi (Alpen-Adria Universität Klagenfurt), Farzad Tashtarian (Alpen-Adria Universität Klagenfurt),  Christian Timmerer (Alpen-Adria Universität Klagenfurt)

Abstract: Live user-generated content (UGC) has increased significantly in video streaming applications. Improving the quality of experience (QoE) for users is a crucial consideration in UGC live streaming, where a user can be both a subscriber and a streamer. Resource allocation is an NP-complete task in UGC live streaming due to many subscribers and streamers with varying requests, bandwidth limitations, and network constraints. In this paper, to decrease the execution time of the resource allocation algorithm, we first process streamers’ and subscribers’ requests and then aggregate them into a limited number of groups based on their preferences. Second, we
perform resource allocation for these groups that we call communities. We formulate the resource allocation problem for communities into an optimization problem. With an efficient aggregation of subscribers and streamers at the core of the proposed architecture, the computational complexity of the optimization problem is reduced, consequently improving QoE. This improvement occurs because of the prompt reaction to the bandwidth fluctuations and, subsequently, appropriate resource allocation by the proposed model. We conduct experiments in various scenarios. The results show an average of 41% improvement in execution time. To evaluate the impact of bandwidth fluctuations on the proposed algorithm, we employ two network traces: AmazonFCC and NYUBUS. The results show 4%, and 28% QoE improvement in a scenario with 5
streamers over the AmazonFCC and the NYUBUS network traces, respectively.

Link: 13th International Conference on Computer and Knowledge Engineering (ICCKE)

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Reza Farahani presented our poster at the 29th International European Conference on Parallel and Distributed Computing (EURO-PAR 2023), Limassol, Cyprus.

Title: Designing A Sustainable Serverless Graph Processing Tool on the Computing Continuum

Abstract: Graph processing has become increasingly popular and essential for solving complex problems in various domains, like social networks. However, processing graphs at a massive scale poses critical challenges, such as inefficient resource and energy utilization. To bridge such challenges, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. This paper presents an initial architectural design for the Choreographer, one of the five Graph-Massivizer tools. We explain Choreographer’s components and their collaboration with other Graph-Massivizer tools. We demonstrate how Choreographer can adopt the emerging serverless computing paradigm to process Basic Graph Operations (BGOs) as serverless functions across the computing continuum efficiently. Moreover, we present an early vision of our federated Function-as-a-Service (FaaS) testbed, which will be used to conduct experiments and assess Choreographer performance.

 

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Kurt Horvath presented his work at EURO-PAR 2023

Kurt Horvath presented the paper titled A distributed geofence-based discovery scheme for the computing Continuum at 29th International European Conference on Parallel and Distributed Computing (EURO-PAR 2023)

Authors: Kurt Horvath, Dragi Kimovski, Christoph Uran, Helmut Wöllik, and Radu Prodan

Abstract: Service discovery is a vital process that enables low latency provisioning of Internet of Things applications across the computing continuum. Unfortunately, it becomes increasingly difficult to identify a proper service within strict time constraints due to the high heterogeneity of the computing continuum. Moreover, the plethora of network technologies and protocols commonly used by the Internet of Things applications further hinders service discovery. To address these issues, we introduce a novel mobile edge service discovery algorithm named Mobile Edge Service Discovery using the DNS (MESDD), which utilizes intermediate code to identify a suitable service instance across the computing continuum based on the naming scheme used to identify the users’ location. MESDD utilizes geofences to aid this process, which enables fine-grained resource discovery. We deployed a real-life distributed computing continuum testbed and compared MESDD with three related methods. The evaluation results show that MESDD outperforms the other approaches by 60% after eight discovery iterations.

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Reza Farahani is giving our first invited talk on unveiling the potential of the EDGE-CLOUD CONTINUUM

A DEEP DIVE INTO VIDEO STREAMING AND GRAPH PROCESSING USE CASES

More information

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IEEE MIPR 2023: Keynote Speaker

Prof. Hermann Hellwagner is a keynote speaker at IEEE MIPR, 30th August – 1st September 2023.

Title: Advances in Edge-Based and In-Network Media Processing for Adaptive Video Streaming

Talk Abstract: Media traffic (mainly, video) on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research was the HTTP Adaptive Streaming (HAS) technique. While this technique is widely used and works well in industrial networked multimedia services today, challenges exist for future multimedia systems, dealing with the trade-offs between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, low latency), and (iii) quality of experience (QoE). This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry.

In this talk, I’ll explore one facet of the ATHENA research, namely how and with which benefits edge-based and in-network media processing can cope with adverse network conditions and/or improve media quality/perception. Content Delivery Networks (CDNs) are the classical example of supporting content distribution on today’s Internet. In recent years, though, techniques like Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Function Virtualization (NFV), Peer Assistance (PA) for CDNs, and Machine Learning (ML) have emerged that can additionally be leveraged to support adaptive video streaming services. In the talk, I’ll present several approaches of edge-based and in-network media processing in support of adaptive streaming, in four groups:

  1. Edge Computing (EC) support, for instance transcoding, content prefetching, and adaptive bitrate algorithms at the edge.
  2. Virtualized Network Function (VNF) support for live video streaming.
  3. Hybrid P2P, Edge and CDN support including content caching, transcoding, and super-resolution at various layers of the system.
  4. Machine Learning (ML) techniques facilitating various (end-to-end) properties of an adaptive streaming system.