The heterogeneity of the network at the edge creates unequal bandwidth availability for clients connected through diverse networks and devices. This variability can lead to bandwidth fluctuations and slow response times, resulting in buffering or degraded streaming performance. As such, effective resource allocation and bandwidth utilization are crucial for optimizing video quality. Live events, which often experience sudden spikes in traffic, present additional challenges in terms of resource management. These challenges are further intensified when malicious users exploit shared bandwidth, placing additional strain on the system. This study is aimed at enhancing adaptive streaming by balancing both security and quality. We integrated watermarking techniques with the analysis of user streaming behavior and traffic patterns in real live streaming platforms within a multi‐CDN (Content Delivery Network) architecture. While Digital Rights Management (DRM) primarily controls access to content, watermarking technologies go a step further by embedding unique identifiers to track content distribution. This capability enables precise monitoring of content, even when sourced from unknown origins, though it comes with added complexity and challenges. By leveraging real‐time system data analytics, we extract critical insights that drive decision‐making, strengthen fraud detection, and optimize resource allocation. Our empirical results show that, along with providing secure live video streaming, having a comprehensive overview of network conditions and traffic distribution can improve Key Performance Indicators (KPIs) in practice by up to 17%.
Kalan et al. (Thu,) studied this question.