Work

A Live Adaptive Streaming Solution for Enhancing Quality of Experience in Co-Created Opera

Year: 2025

Type: article

Abstract: The collaborative nature of opera production offers a unique opportunity to strengthen societal cohesion and empower marginalized voices through storytelling. However, existing live streaming approaches, such as HTTP-Adaptive Streaming (HAS), are not equipped to handle the complexities of co-created opera content, resulting in suboptimal user experiences. To address these limitations, this article introduces the Live Stream Adaptation for Opera (LSAO), a solution designed as part of the EU Horizon 2020 TRACTION project. LSAO is a network-aware adaptive scheme designed to optimize the delivery of live co-created opera performances by dynamically adjusting audiovisual quality based on varying network conditions. Unlike traditional streaming solutions, LSAO prioritizes the unique demands of opera, ensuring seamless delivery and preserving artistic features. The evaluation of LSAO involved an online live opera show featuring four distinct performances by six artists located in globally distributed locations. Delivered to 35 remote viewers across 12 countries and 3 continents, the LSAO system was evaluated based on user feedback on the quality of their streaming experience. The results demonstrate the effectiveness of LSAO in enhancing audio and video quality levels, leading to heightened user enjoyment during live co-created opera performances. Through its approach and successful evaluation, LSAO represents a significant advancement in the delivery of live co-created opera content.

Source: IEEE Transactions on Broadcasting

DOI: 10.1109/tbc.2025.3541875

Authors: Rohit Verma, Anderson Augusto Simiscuka, Mohammed Amine Togou, Mikel Zorrilla, Gabriel-Miro Muntean

Institutions: Technological University Dublin, Dublin City University, Insight SFI Research Centre for Data Analytics, Vicomtech

Language: English

Topic: Image and Video Quality Assessment

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,645