Delivering high streaming quality can be more challenging than ever given the wide range of devices and operating systems that video service providers are expected to flawlessly support. As providers look to streamline the quality-assurance process (QA) and deliver outstanding streaming quality, AI-powered video testing and monitoring is transforming workflows. With AI video testing, video service providers can efficiently evaluate video compression, test real-time live content and assess video quality without needing reference streams. AI-driven video testing is supplying providers with valuable insights to improve streaming quality, build brand awareness and drive viewer retention.
The Challenges of Video Testing and Monitoring
The video streaming landscape is rapidly evolving, posing complex challenges for video service software development. In the past, software development followed a more linear approach, with a new release every few months in a controlled update path. Today, agile development has dramatically increased the frequency of updates, requiring weekly—and sometimes even daily —new releases across various platforms and systems. Unlike traditional cable or satellite systems, where stability in encryption and broadcasting minimized the need for frequent updates, streaming video requires constant adaptation.
Modern streaming services are expected to perform identically well on a multitude of devices and operating systems backed by various cloud environments, all of which are constantly evolving. Even the simplest of video app features relies on seamless integration between multiple layers: the application running on the device, which runs on a proprietary operating system, often with multiple cloud-to-cloud service integrations. Each of those components has its own lifecycle and can be updated at any time.
As partnerships look to become an even larger part of the ever-shifting market, QA teams face immense pressure to maintain functionality and reliability across a wide range of devices and services.”
To look at just one set of devices, smart TVs in 2024 run on more than 12 different operating systems, with LG even shipping new updates only for its most recent models and keeping older models on a different software version. Hisense provides five different operating systems for its TVs in addition to the Vidaa operating system it developed in-house. Providers need to find a way to test how their service works on all of these devices and operating systems to deliver optimal quality.
The complexity of testing increases as service providers launch partnerships. Providers like Verizon now offer packaged subscriptions to third-party streaming services like Peacock, which means they must ensure seamless performance not only for their own platform but also across partner applications. A third-party app failure could harm the provider’s brand, pushing users away. As partnerships look to become an even larger part of the ever-shifting market, QA teams face immense pressure to maintain functionality and reliability across a wide range of devices and services.
How AI is Transforming Testing and Monitoring
AI plays a crucial role in transforming the QA testing process by enabling scalable, efficient testing that meets the demands of fast-paced software development. Through automation, AI, and machine learning, companies can test complex systems at a level that would be impossible with manual testing, saving valuable resources and time in an industry pushing for profitability.
An example of AI’s usefulness is in enhancing how video quality is measured and optimized during the encoding process. Netflix’s open-source VMAF model, for instance, allows compression algorithms to evaluate video degradation by comparing the original and compressed versions of a video stream, identifying how much quality was lost. However, because VMAF is computationally heavy, providers often opt for lighter, customized versions, such as pVMAF, which is computed on multiple encoding profiles, to help select the best bit rate quality balance.
This approach relies on the availability of reference streams, which are usually unavailable for live sports or user-generated content. Viewers at home also don’t have a reference stream to assess video quality—they just evaluate the performance themselves. AI algorithms can be programmed to reliably perform this non-reference-based quality assessment on real devices, allowing video service providers to maintain quality control at scale, testing without sacrificing speed or accuracy.
The Role of AI in Testing Live Streaming Events
Real-time AI testing can help providers prevent disruptions during high-traffic live streaming events. The field of research includes anomaly detection and issue prevention, especially on the field of multi-CDN delivery. Providers may choose to disable computationally expensive features, such as targeted ads or additional security layers, to preserve scaling capabilities.
Results From AI-Enhanced Video Testing and Monitoring
AI is also being used to enhance the quality and reliability of targeted ads by reducing failure rates in Dynamic Ad Insertion, which directly impacts revenue. By analyzing and monitoring slate occurrences—instances where an ad fails to load properly—AI helps identify patterns, working collaboratively across development, ad generation and ad provider teams to improve ad placement success.
AI insights have also guided providers toward a balance between visual quality and network efficiency. Delivering “good enough” video quality on mobile networks—consistent with user expectations and competitive standards—enables service providers to avoid excessive data consumption that could cause disruptions or resolution drops. For instance, streaming a video at a 4K resolution consumes seven times the amount of bandwidth as the industry standard of 1080p, when it does not necessarily provide a viewing experience that is seven times better. Optimizing this balance ensures a smoother viewing experience, improving engagement without sacrificing reliability on mobile networks.
Relying on AI-Powered Video Testing and Monitoring
AI is an essential tool for meeting the complex demands of video streaming, allowing QA teams to scale testing and monitor performance in real time. Since streaming platforms and partnerships evolve frequently, traditional testing methods can’t keep pace. AI-driven testing adapts to these rapid changes, simulating user experiences across various devices without requiring deep integration into each app or operating system. Rather than trying to develop new testing technology from scratch, relying on powerful AI-driven testing and monitoring technology allows talented teams to put their time and effort back into their video service. This agile, user-centric AI-powered technology prepares providers to tackle emerging trends and anticipate future challenges, enabling them to focus on enhancing service quality and user satisfaction.
