Paper accepted: ALPHAS: Adaptive Bitrate Ladder Optimization for Multi-Live Video Streaming

Authors: Farzad Tashtarian (Alpen-Adria Universität Klagenfurt, Austria); Mahdi Dolati (Sharif University of Technology, Iran); Daniele Lorenzi (University of Klagenfurt, Austria); Mojtaba Mozhganfar (University of Tehran, Iran); Sergey Gorinsky (IMDEA Networks Institute, Spain); Ahmad Khonsari (University of Tehran, Iran); Christian Timmerer (Alpen-Adria-Universität Klagenfurt & Bitmovin, Austria); Hermann Hellwagner (Klagenfurt University, Austria)

Event: IEEE INFOCOM 2025,  19–22 May 2025 // London, United Kingdom

Abstract: Live streaming routinely relies on the Hypertext Transfer Protocol (HTTP) and content delivery networks (CDNs) to scalably disseminate videos to diverse clients. A bitrate ladder refers to a list of bitrate-resolution pairs, or representations, used for encoding a video. A promising trend in HTTP-based video streaming is to adapt not only the client’s representation choice but also the bitrate ladder during the streaming session. This paper examines the problem of multi-live streaming, where an encoding service performs coordinated CDN-aware bitrate ladder adaptation for multiple live streams delivered to heterogeneous clients in different zones via CDN edge servers. We design ALPHAS, a practical and scalable system for multi-live streaming that accounts for CDNs’ bandwidth constraints and encoder’s computational capabilities and also supports stream prioritization. ALPHAS, aware of both video content and streaming context, seamlessly integrates with the end-to-end streaming pipeline and operates in real time transparently to clients and encoding algorithms. We develop a cloud-based ALPHAS implementation and evaluate it through extensive real-world and trace-driven experiments against four prominent baselines that encode each stream independently. The evaluation shows that ALPHAS outperforms the baselines, improving quality of experience, end-to-end latency, and per-stream processing by up to 23%, 21%, and 49%, respectively.

 

Paper accepted: Generative AI for Realistic Voice Dubbing Across Languages

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

Event: ACM 4th Mile-High Video Conference (MHV’25), 18–20 February 2025 |
Denver, CO, USA

Abstract: The demand for accessible, multilingual video content has grown significantly with the global rise of streaming platforms, social media, and online learning. The traditional solutions for making content accessible across languages include subtitles, even generated ones, as YouTube offers, and synthesizing voiceovers, offered, for example, by the Yandex Browser. Subtitles are cost-effective and reflect the original voice of the speaker, which is often essential for authenticity. However, they require viewers to divide their attention between reading text and watching visuals, which can diminish engagement, especially for highly visual content. Synthesized voiceovers, on the other hand, eliminate this need by providing an auditory translation. Still, they typically lack the emotional depth and unique vocal characteristics of the original speaker, which can affect the viewing experience and disconnect audiences from the intended pathos of the content. A straightforward solution would involve having the original actor “perform” in every language, thereby preserving the traits that define their character or narration style. However, recording actors in multiple languages is impractical, time-intensive, and expensive, especially for widely distributed media.

By leveraging generative AI, we aim to develop a client-side tool, to incorporate in a dedicated video streaming player, that combines the accessibility of multilingual dubbing with the authenticity of the original speaker’s performance, effectively allowing a single actor to deliver their voice in any language. To the best of our knowledge, no current streaming system can capture the speaker’s unique voice or emotional tone.

Paper accepted: Adaptive Quality and Energy Enhancement in Video Streaming with RecABR

Authors: Daniele Lorenzi (Alpen-Adria Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria Universität Klagenfurt, Austria)

Event: ACM 4th Mile-High Video Conference (MHV’25), 18–20 February 2025 |
Denver, CO, USA

Abstract: HTTP Adaptive Streaming (HAS) dominates video delivery but faces sustainability issues due to its energy demands. Current adaptive bitrate (ABR) algorithms prioritize quality, neglecting the energy costs of higher bitrates. Super-resolution (SR) can enhance quality but increases energy use, especially for GPU-equipped devices in competitive networks. RecABR addresses these challenges by clustering clients based on device attributes (e.g., GPU, resolution) and optimizing parameters via linear programming. This reduces computational overhead and ensures energy-efficient, quality-aware recommendations. Using metrics like VMAF and compressed SR models, RecABR minimizes storage and processing costs, making it scalable for CDN edge deployment.

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Magdalena Strobl @ “Game Over?” – Conference, Salzburg

Magdalena participated in the “Game Over?”- Conference from November 14th to 16th with its topic “Dystopia x Utopia x Video Games.” Together with Iris van der Horst (MEd), she presented the talk titled “From Oppression to Liberation: Postcolonial Perspectives on the Dystopian World of Xenoblade Chronicles 3” focused on how the game displays a critical dystopia variant 1 and how it criticizes colonial structures by magnifying the abstract concepts of third space and contact zone and depicting them in a very concrete and creative way. Therefore, this presentation combined theories on utopianism and postcolonialism.

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Paper accepted: PreMatch: A Prediction-based Scheduler for Dataflow Asynchronous Processing on the Computing Continuum

Authors: Narges Mehran, Zahra Najafabadi Samani, Samira Afzal, Radu Prodan, Frank Pallas and Peter Dorfinger

Event: The 40th ACM/SIGAPP Symposium On Applied Computing https://www.sigapp.org/sac/sac2025/

Abstract:

The popularity of asynchronous data exchange patterns has recently increased, as evidenced by an Alibaba trace analysis showing that 23% of the communication between microservices uses this method. Such workloads necessitate exploring a method for reducing their dataflow processing and completion time. Moreover, there is a need to exploit a prediction method to forecast the future requirements of such microservices and (re-)schedule them. Therefore, we investigate the prediction-based scheduling of asynchronous dataflow processing applications by considering the stochastic changes due to dynamic user requirements.

Moreover, we present a microservice scaling and scheduling method named PreMatch combining a machine learning prediction strategy based on gradient boosting with ranking and game theory matching scheduling principles. Firstly, PreMatch predicts the number of microservice replicas, and then, the ranking method orders the microservice replica and devices based on microservice and transmission times. Thereafter, the PreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis of the PreMatch method shows lower completion times on average 13% compared to a related prediction-based scheduling method.

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Workshop paper accepted: One Spell Fits All: A Generative AI Game as a Tool for Research in AI Creativity and Sustainable Design

Authors: Tom Tucek, Kseniia Harshina, Georgia Samaritaki (University of Amsterdam), and Dipika Rajesh (University of California, Santa Cruz)

Abstract:
This paper presents “One Spell Fits All”, an AI-native game prototype where the player, playing as a witch, solves villagers’ problems using magical conjurations. We show how, beyond being a standalone game, “One Spell Fits All” could serve as a research platform to explore several key areas in AI-driven and AI-native game design. These areas include AI creativity, user experience in predominantly AI-generated content, and the energy efficiency of locally running versus cloud-based AI models. By leveraging smaller, locally running generative AI models, including LLMs and diffusion models for image generation, the game dynamically generates and evaluates content without the need for external APIs or internet access, offering a sustainable and responsive gameplay experience. This paper explores the application of LLMs in narrative video games, outlines a game prototype’s design and mechanics, and proposes future research opportunities that can be explored using the game as a platform.

EXAG ’24: Experimental AI in Games Workshop at the AIIDE Conference, November 18, 2024, Lexington, USA

 

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Short paper accepted: Experiencing Forced Migration: A Participatory Approach to Developing a Serious Video Game

Authors: Kseniia Harshina, Imke Alenka Harbig, Mathias Lux, and Tom Tucek

Abstract: Forced migration affects millions worldwide due to conflict, disasters, and persecution. This paper presents a participatory approach to serious game development, engaging individuals with lived migration experiences to create authentic and impactful narratives. Based on qualitative research, we propose a game design methodology to enhance empathy and raise awareness of forced migration. Our study demonstrates the potential of participatory games to bridge the gap between forced migrants and the broader public, fostering empathy, social change, and empowerment for marginalized communities.

GALA Conference 2024 Proceedings will be published on Springer Lecture Notes in Computer Science. Kseniia and Imke Harbig from the psychology department will present their research at the GALA conference in Berlin from November 20 to 22.

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Paper accepted: PagerBombs and Intelligent Vehicles: Stop Weaponing Supply Chains with Blockchain Intelligence

Authors: Jing Yang, Qinghua Ni, Song Zhang, Nan Zheng, Juanjuan Li, Lili Fan, Lili Fan, Radu Prodan, Levente Kovacs, Fei-Yue Wang

IEEE Transactions on Intelligent Vehicles: https://ieeexplore.ieee.org/document/10734076/

Abstract: The Lebanese wireless device explosion incident has drawn widespread attention, involving devices such as pagers, walkie-talkies, and other common devices. This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment, signaling the onset of a new wave of “supply chain warfare”. Even worse, with the rapid proliferation of Internet of Things (IoT) devices and smart hardware, the fragility of global supply chains would become increasingly fatal and significant, since almost all devices of daily usage could be maliciously programmed and triggered as weapons of massive destruction. Given this, we need new thinking and new approaches to improve supply chain security [3]. With its decentralized, tamper-proof, and highly traceable characteristics, blockchain technology is considered an effective solution to address these security threats [4], [5]. How to secure the entire lifecycle of smart devices, from production and transportation to usage, through blockchain-enabled safety management and protection, has become a pressing issue that requires immediate attention.

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Paper accepted: In-Band Quality Notification from Users to ISPs

Authors: Leonardo Peroni (UC3M, Spain); Sergey Gorinsky (IMDEA Networks Institute, Spain); Farzad Tashtarian (Alpen-Adria Universität Klagenfurt, Austria)

Conference: IEEE 13th International Conference on Cloud Networking (CloudNet)

27–29 November 2024 // Rio de Janeiro, Brazil

Abstract: While ISPs (Internet service providers) strive to improve QoE (quality of experience) for end users, end-to-end traffic encryption by OTT (over-the-top) providers undermines independent inference of QoE by an ISP. Due to the economic and technological complexity of the modern Internet, ISP-side QoE inference based on OTT assistance or out-of-band signaling sees low adoption. This paper presents IQN (in-band quality notification), a novel mechanism for signaling QoE impairments from an automated agent on the end-user device to the server-to-client ISP responsible for QoE-impairing congestion. Compatible with multi-ISP paths, asymmetric routing, and other Internet realities, IQN does not require OTT support and induces the OTT server to emit distinctive packet patterns that encode QoE information, enabling ISPs to infer QoE by monitoring these patterns in network traffic. We develop a prototype system, YouStall, which applies IQN signaling to ISP-side inference of YouTube stalls.
Cloud-based experiments with YouStall on YouTube Live streams validate IQN’s feasibility and effectiveness, demonstrating its potential for accurate user-assisted ISP-side QoE inference from encrypted traffic in real Internet environments.

Hadi
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Paper accepted: Energy-Efficient Video Streaming: A Study on Bit Depth and Color Subsampling

Authors: Hadi Amirpour (AAU, Austria), Lingfeng Qu (Guangzhou University, China), Jong Hwan Ko (SKKU, South Korea), Cosmin Stejerean (Meta, USA), Christian Timmerer (AAU, Austria

Conference: IEEE Visual Communications and Image Processing (IEEE VCIP 2024)  – Tokyo, Japan, December 8-11, 2024

Abtract: As video dimensions — including resolution, frame rate, and bit depth — increase, a larger bitrate is required to maintain a higher Quality of Experience (QoE). While videos are often optimized for resolution and frame rate to improve compression and energy efficiency, the impact of color space is often overlooked. Larger color spaces are essential for avoiding color banding and delivering High Dynamic Range (HDR) content with richer, more accurate colors, although this comes at the cost of higher processing energy. This paper investigates the effects of bit depth and color subsampling on video compression efficiency and energy consumption. By analyzing different bit depths and subsampling schemes, we aim to determine optimized settings that balance compression efficiency with energy consumption, ultimately contributing to more sustainable and high-quality video delivery. We evaluate both encoding and decoding energy consumption and assess the quality of videos using various metrics including PSNR, VMAF, ColorVideoVDP, and CAMBI. Our findings offer valuable insights for video codec developers and content providers aiming to improve the performance and environmental footprint of their video streaming services.

Index Terms— Video encoding, video decoding, video quality, bit depth, color subsampling, energy.