PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 24, 2025Computers0 citationsOpen Access

Security-Aware Adaptive Video Streaming via Watermarking: Tackling Time-to-First-Byte Delays and QoE Issues in Live Video Delivery Systems

View Full Paper
RKReza Shokri KalanPCPeren Jerfi CanatalayEKEmre Karslı

Key Points

  • Watermarking can slow video delivery by increasing latency, impacting user experience significantly.
  • Addressing CDN errors improved performance metrics by 6%, showcasing enhanced stability and video quality.
  • Optimizing system architecture with a shield cache reduced processing load and minimized delays.
  • Network congestion can lead to critical errors, negatively affecting QoE in live video delivery systems.

Abstract

Illegal broadcasting is one of the primary challenges for Over the Top (OTT) service providers. Watermarking is a method used to trace illegal redistribution of video content. However, watermarking introduces processing overhead due to the embedding of unique patterns into the video content, which results in additional latency. End-to-end network latency, caused by network congestion or heavy load on the origin server, can slow data transmission, impacting the time it takes for the segment to reach the client. This paper addresses 5xx errors (e.g., 503, 504) at the Content Delivery Network (CDN) in real-world video streaming platforms, which can negatively impact Quality of Experience (QoE), particularly when watermarking techniques are employed. To address the performance issues caused by the integration of watermarking technology, we enhanced the system architecture by introducing and optimizing a shield cache in front of the packager at the origin server and fine-tuning the CDN configuration. These optimizations significantly reduced the processing load on the packager, minimized latency, and improved overall content delivery. As a result, we achieved a 6% improvement in the Key Performance Indicator (KPI), reflecting enhanced system stability and video quality.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Kalan et al. (2025) studied this question.

synapsesocial.com/papers/68d6d8978b2b6861e4c3eb47https://doi.org/10.3390/computers14100404
Ask AI
Helpful
Bookmark
Share
View Full Paper