Media companies face more complex challenges than ever in a fragmented, fast-evolving landscape. Delivering live video across multiple platforms and geographies requires robust and scalable video transport solutions. To meet today’s required scale and unlock further monetization potential, broadcasters need viable and reliable alternatives to satellite and fiber, which are both costly and limiting. Navigating change and embracing new technologies can be daunting – but with the right technology partner and best-in-class solutions and services, media companies can achieve order and peace of mind in a challenging and frequently chaotic ecosystem.
Producing and distributing live video events has evolved. A move to increased live event production and new audience experiences that had already been underway was greatly accelerated with the pandemic.
Celebrating its 19th year in business, LYNX Technik is a global manufacturer of signal processing solutions and modular interface equipment. The company is based in Weiterstadt, Germany, with offices in Los Angeles (U.S.) and Singapore. TheBroadcastBridge.com spoke with David Holloway, Director of Sales EMEA, at LYNX Technik.
Our third and final part of this series looks at Quality Control & Compliance, which bring efficiency, confidence, and legal conformance to daily TV station operations.
IP is incredibly versatile. It’s data payload agnostic and multiple transport streams have the capability to transport it over many different types of networks. However, this versatility provides many challenges, especially when sending video and audio over networks.
We continue our series on things to consider when designing broadcast audio systems with the beating heart of all things audio – the mixing console.
Remote Integration Model (REMI) production is more than remote cameras. It’s a new way of thinking and working. This tale of trying to implement a REMI production model within tight financial constraints highlights some of the operational challenges involved.
In part one of this series, we looked at why machine learning, with particular emphasis on neural networks, is different to traditional methods of statistical based classification and prediction. In this article, we investigate some of the applications specific for broadcasters that ML excels at.