Advancing Digital Advertising: A Technical Deep Dive into Real-Time Ad Injection and HLS Streaming

Authors

  • Indrajit Shanbhag Senior Software Engineer, United States Author

DOI:

https://doi.org/10.32628/CSEIT25111710

Keywords:

Real-Time Ad Injection, HLS Streaming, Server-Side Ad Insertion, Low-Latency HLS, Digital Advertising, SCTE-35

Abstract

Over-the-Top (OTT) platforms have become more abundant, and at the same time, with the support of adaptive streaming protocols such as HTTP Live Streaming (HLS), a very dynamic but complex environment has emerged for digital advertising. There is high demand for scalable, targeted ad monetization, which would also be non-disruptive. Current methodologies not only face resistance from ad blockers but also introduce latency-induced buffering as well as fragmentation between legacy broadcast and modern digital ecosystems. This paper presents an original unified framework for real-time ad injection within HLS environments. They are three-fold: improved Server-Side Ad Insertion (SSAI) architecture capable of facilitating dynamic ad decisioning and seamless ad stitching, a hybrid model intended to connect advanced cloud-native ad servers to legacy broadcast infrastructures via standards-based workflows; technical innovations, among which are Low-Latency HLS (LL-HLS) optimizations for reducing buffering as well as opening up the transparency of the ad-tracking systems. Proof-of-concept results show a drastic reduction in the ad-failure rate, plus improved targeting accuracy and consistency of playbacks from different devices. In the end, this work acts as an easy-to-use and scalable pathway for media houses and content providers to maximize their revenues by ensuring continuous, seamless viewing experiences that put real-time HLS ad insertion at the heart of next-generation digital advertising.

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References

A. K. M. K. H. A. S. K. Das, "A Low-Latency and Bandwidth-Efficient Framework for Server-Side Ad Insertion in Live HLS Streaming," in IEEE Transactions on Broadcasting, vol. 67, no. 4, pp. 842-856, Dec. 2021.

Y. Li, Z. Chen, and H. Liu, "An Intelligent Ad Insertion System for OTT Platforms Based on Deep Reinforcement Learning," in IEEE Access, vol. 10, pp. 45672-45683, 2022.

J. Park, S. Lee, and M. Kim, "A Hybrid Client-Server Ad Insertion Model for Privacy-Compliant and Robust CTV Advertising," in IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2023, pp. 1-4.

M. Gupta, R. K. P. K. J. Wang, "Synchronized Multi-Screen Ad Delivery: A Novel SCTE-35 Based Framework for Unified Broadcast and OTT Streams," in IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Beijing, China, 2022, pp. 1-6.

S. R. T. H. W. X. Liu, "Optimizing QoE in LL-HLS with Machine Learning-Based Adaptive Bitrate and Ad Insertion," in IEEE Transactions on Multimedia, vol. 25, pp. 4567-4580, 2023.

None Murali Natti(2023). “Reducing postgreSQL read and write latencies through optimized fillfactor and hot percentages for high-update applications,” International Journal of Science and Research Archive, vol. 9, no. 2, pp. 1059–1062. doi: https://doi.org/10.30574/ijsra.2023.9.2.0657. DOI: https://doi.org/10.30574/ijsra.2023.9.2.0657

A. Fernandez, P. Serrano, and A. Banchs, "A Server-Side Architecture for Context-Aware and Privacy-Preserving Ad Insertion in Adaptive Video Streaming," in IEEE Journal on Selected Areas in Communications, vol. 42, no. 4, pp. 982-997, April 2024.

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Published

10-10-2025

Issue

Section

Research Articles

How to Cite

[1]
Indrajit Shanbhag, “Advancing Digital Advertising: A Technical Deep Dive into Real-Time Ad Injection and HLS Streaming”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 11, no. 5, pp. 188–197, Oct. 2025, doi: 10.32628/CSEIT25111710.