Technical and Operational Evaluation of SRT-Based IP Contribution for Live Television Broadcasting

Authors

DOI:

https://doi.org/10.26877/asset.v8i4.3710

Keywords:

IP contribution, live broadcasting, Quality of Service, remote production, Secure Reliable Transport

Abstract

Secure Reliable Transport (SRT) is increasingly used for IP-based live-broadcast contribution because it enables low-latency delivery, packet recovery, and encrypted transport over public networks. However, its practical value depends on measurable performance under real operating conditions and disciplined operational control. This study evaluates the implementation of SRT in live football league and volleyball broadcasts using bounded case evidence from operational records, questionnaires, and focused interviews. The analysis links event-level Quality of Service (QoS) behavior with operational deployment indicators and compares SRT-based contribution with conventional IP transport and satellite contribution. The evaluated indicators include latency, effective packet loss after recovery, service interruption events, recovery time, bitrate stability, setup time, onsite staffing, and operational expenditure. The findings show that SRT provides a practical latency-reliability trade-off relative to regular best-effort IP transport and a lower deployment-cost profile than satellite contribution under the observed conditions. Stable implementation depends on consistent parameter settings, redundancy planning, monitoring ownership, and incident escalation discipline. This study offers a reproducible, engineering-oriented case-evaluation structure for assessing SRT deployment and improving reliability and operational efficiency in IP-based live broadcasting.

Author Biographies

  • Ciptono Setyo Budi, Universitas Trisakti

    Doctoral Program in Industrial Engineering, Faculty of Engineering, Universitas Trisakti, Jl. Kyai Tapa No.1, West Jakarta 11440, Indonesia

  • Triwulandari Satitidjati Dewayana, Universitas Trisakti

    Doctoral Program in Industrial Engineering, Faculty of Engineering, Universitas Trisakti, Jl. Kyai Tapa No.1, West Jakarta 11440, Indonesia

  • Emelia Sari, Universitas Trisakti

    Doctoral Program in Industrial Engineering, Faculty of Engineering, Universitas Trisakti, Jl. Kyai Tapa No.1, West Jakarta 11440, Indonesia

  • Indra Surjati, Universitas Trisakti

    Doctoral Program in Industrial Engineering, Faculty of Engineering, Universitas Trisakti, Jl. Kyai Tapa No.1, West Jakarta 11440, Indonesia

References

[1] M. P. Sharabayko, M. A. Sharabayko. The Secure Reliable Transport (SRT) Protocol. IETF Internet-Draft, draft-sharabayko-srt-00; 2020.

[2] M. Sharabayko. SRT Protocol Overview. IETF 107 Dispatch/MOPS presentation; 2020.

[3] Haivision Systems Inc. Secure Reliable Transport (SRT) Protocol Technical Overview. SRT Alliance; 2018.

[4] Y. Reznik, J. Cenzano, B. Zhang. Transitioning Broadcast to Cloud. SMPTE Motion Imaging J. 2021;130(9):18–32. https://doi.org/10.5594/JMI.2021.3106162.

[5] Y. Wu, X. Bai, Y. Hu, M. Chen. A novel video transmission latency measurement method for intelligent cloud computing. Appl Sci. 2022;12(24):12884. https://doi.org/10.3390/app122412884.

[6] T. Guo, Z. Song, H. Xin, G. Liu. A decentralized multi-venue real-time video broadcasting system integrating chain topology and intelligent self-healing mechanisms. 2025. p. 1–39. https://doi.org/10.3390/app15148043

[7] R. Belda, P. Arce, I. De Fez, J. C. Guerri. Performance evaluation and testbed for delivering SRT live content using DASH low latency streaming systems. PE-WASUN 2022 - Proc 19th ACM Int Symp Perform Eval Wirel Ad Hoc Sensor Ubiquitous Networks. 2022. p. 115–121. https://doi.org/10.1145/3551663.3558674.

[8] N. Al-imareen, G. Lencse. Enhancing real-time video streaming quality via MPT-GRE multipath network. 2025. p. 1–20. https://doi.org/10.3390/electronics14030497

[9] J. Bienik, M. Uhrina, L. Sevcik, A. Holesova. Impact of packet loss rate on quality of compressed high resolution videos. Sensors. 2023;23(5):2744. https://doi.org/10.3390/s23052744.

[10] S. Uddin, M. Grega, M. Leszczuk, W. ur Rahman. Evaluating HAS and low-latency streaming algorithms for enhanced QoE. Electronics. 2025;14(13):2587. https://doi.org/10.3390/electronics14132587.

[11] J. Farquhar, N. Michels, J. Robson. Triangulation in industrial qualitative case study research: widening the scope. Ind Mark Manag. 2020;87:160–170. https://doi.org/10.1016/j.indmarman.2020.02.001.

[12] U. Östlund, L. Kidd, Y. Wengström, N. Rowa-Dewar. Combining qualitative and quantitative research within mixed method research designs: a methodological review. Int J Nurs Stud. 2011;48(3):369–383. https://doi.org/10.1016/j.ijnurstu.2010.10.005.

[13] M. O. Elbasheer, A. Aldegheishem, N. Alrajeh, J. Lloret. Video streaming adaptive QoS routing with resource reservation (VQoSRR) model for SDN networks. Electronics. 2022;11(8):1252. https://doi.org/10.3390/electronics11081252.

[14] T. Lyko, M. Broadbent, N. Race, M. Nilsson, P. Farrow, S. Appleby. Improving quality of experience in adaptive low latency live streaming. Multimed Tools Appl. 2024;83:15957–15983. https://doi.org/10.1007/s11042-023-15895-9.

[15] N. Barman, M. G. Martini. QoE modeling for HTTP adaptive video streaming: a survey and open challenges. IEEE Access. 2019;7:30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778.

[16] A. M. T. Thomé, L. F. Scavarda, A. J. Scavarda. Conducting systematic literature review in operations management. Prod Plan Control. 2016;27(5):408–420. https://doi.org/10.1080/09537287.2015.1129464.

[17] SRT Alliance. SRT Deployment Guide, Version 1.1. SRT Alliance; 2017.

[18] T. N. Minhas, M. Fiedler. Mitigation of the effects of network outage on video QoE using a sender buffer. Electronics. 2021;10(10):1209. https://doi.org/10.3390/electronics10101209.

[19] ITU-T. Recommendation Y.1540: Internet protocol data communication service - IP packet transfer and availability performance parameters. International Telecommunication Union; 2019.

[20] SMPTE. Seamless Protection Switching of SMPTE ST 2022 IP Datagrams. 2022;10601(914):1–13.

[21] Shafiatullaily, S. Soim, Nasron. Analysis of Listen Before Talk Protocol Implementation in Terrestrial Network Using Multi-Node LoRa Communication. Adv Sustain Sci Eng Technol. 2023;5(3):1–9. https://doi.org/10.26877/asset.v5i3.16520.

[22] M. Arora, I. McClenaghan. Decarbonizing the media, broadcast, and streaming industry. Joule. 2024;8(4):873–878. https://doi.org/10.1016/j.joule.2024.03.003.

[23] C. Gutterman, B. Fridman, T. Gilliland, Y. Hu, G. Zussman. Stallion: video adaptation algorithm for low-latency video streaming. In: Proc 11th ACM Multimedia Systems Conf (MMSys '20); 2020. p. 327–332. https://doi.org/10.1145/3339825.3397044.

[24] S. Emara, S. L. Fong, B. Li, A. Khisti, W.-T. Tan, X. Zhu, J. Apostolopoulos. Low-latency network-adaptive error control for interactive streaming. IEEE Trans Multimed. 2022;24:1691–1706. https://doi.org/10.1109/TMM.2021.3070134.

[25] J. Nightingale, P. Salva-Garcia, J. M. Alcaraz Calero, Q. Wang. 5G-QoE: QoE modelling for Ultra-HD video streaming in 5G networks. IEEE Trans Broadcast. 2018;64(2):621–634. https://doi.org/10.1109/TBC.2018.2816786.

Downloads

Published

2026-08-29

Issue

Vol. 8 No. 4 (2026): August-October

Section

Articles

License

Copyright (c) 2026 Advance Sustainable Science Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.