Product Engineering

Deliver high-quality entertainment services at high speed and with flawless quality

Automate the end-to-end compatibility testing and rollout steps to deliver a seamless viewing experience across multiple digital platforms and form factors

A series of technological advancements has completely changed the way people consume video content. Compared to earlier days, when a television set was the primary source to consume videos, today’s consumers have many other options – smart TV, streaming box/stick, gaming consoles, DVR, set-top box, tablet, computer, mobile, etc. A recent ComScore OTT state report clearly shows the growing penetration of different digital devices among U.S households.

To deliver a seamless viewing experience, service providers need to ensure video compatibility across a broad range of device types, operating systems, browsers, and network types.

The end-users now have the flexibility to watch their preferred videos on any digital platform of their choice without being much concerned about the supporting operating systems, browsers, and network connectivity.

But if we look from the lens of service providers, delivering video service faster and with high quality has become much more complicated. It requires them to ensure feature compatibility with a broad range of device types with different operating systems, browsers, and network connectivity. This requires a humongous amount of testing in the background. And as digital-savvy users expect feature updates at lightning speed, service providers cannot afford to spend much time testing and rolling out services.

This mandates service providers to technically upgrade their way of working, the testing process, and existing release platforms.

Product Engineering

Use AI to Bolster your Network Capacity Planning decisions

The Content Delivery Network (CDN) market is poised to explode as content consumption gains more momentum. This calls for an efficiency-focused approach towards CDN capacity planning.

As per a Cisco report, the annual global IP traffic has already crossed the zettabyte (ZB) threshold. To cope with the increased content consumption by users, more supply chains should be established along with a reliable and scalable infrastructure. This puts a lot more pressure on the Content Delivery Networks (CDNs), which forms a well-established global backbone for content delivery.

For service providers, it becomes vital to take an efficiency-focused approach towards CDN capacity planning. This means satisfying the future capacity requirements without increasing the total cost of ownership.

The legacy manual way of capacity planning uses basic statistical tools to collect data and set a static threshold on capacity requirements. Such manual planning typically does not analyze the network in a holistic manner and produces a final proposal with a “one rule fits all” approach. However, this approach is inefficient in today’s scenario where consumer behavior changes very dynamically. Manual planning is also prone to human error, so the outcome might deviate from time to time, wasting a substantial number of resources and time. The service providers often run out of capacity due to increased data consumption and changes in the consumption patterns, which are not identified correctly during capacity planning.

To satisfy the customer demands in a timely fashion, it is necessary to have a modern capacity planning strategy.

Network planners need to confront these challenges before it impacts the customer experience. Leveraging Artificial Intelligence (AI) can significantly improve network capacity planning, thereby improving the end-user experience and reducing the total cost of ownership.

Media & Entertainment

Deliver uninterrupted, high-quality entertainment services

Build an effective monitoring framework to ensure high performance of microservices-based streaming services

The multi-fold increase in video content consumption and the different types of devices like mobiles, laptops, and smart TVs used to consume this content have made service providers move towards microservices. Most of the forward-thinking service providers have started adopting microservice-based architecture for the Video-on-Demand (VoD) services to handle the huge number of requests with minimum response time. Further, it enables scalability and continuous deployment of complex applications, thus providing uninterrupted entertainment services. Adoption of microservices-based applications helps to reap benefits such as:

  • Deliver video at scale to meet billions of customer requests each day
  • Handle the load spikes efficiently during special events (e.g. Premier League football games)
  • Ensure reliable delivery and availability of video content services
  • Implement auto-scaling algorithms to save cost by running at optimum capacity during silent hours

The adoption of microservices-based applications helps service providers to deliver entertainment services at scale, meeting billions of customer requests each day.

However, monitoring microservice-based applications is a highly complex task as a single application runs on multiple hosts in a very dynamic environment. It also needs to interact with several other systems that are dynamic. Implementing the right toolchain is critical for effective performance monitoring of microservice-based applications.