This is our second blog in our series on "Understanding the Second Wave of Video Optimization" , which was inspired by the recent Heavy Reading Report on Mobile Video Optimization. Note that the full Heavy Reading report requires a subscription, but you can check out the executive summary and table of contents without one; definitely worth checking out.
In the first blog in the series we talked about the different ingredients that have driven the market to the second wave of video optimization – which we agree is indeed happening. Now we'd like to turn the discussion toward what service providers can do about it. For this blog we'll discuss adding intelligence to optimization.
Why add intelligence to optimization? One of the primary drivers for operators is revenue preservation. As we mentioned in the previous post, operators increasingly rely on data volume for revenue due to capped data buckets. They don't necessarily want to implement a solution that reduces volume and, in turn, bucket overages. However, it is more than just revenue protection feeding the push for adding intelligence to optimization. Operators also want to avoid degrading the user's overall experience by offering a congested network to that user and, in fact, want to maximize the experience based on what the network can handle. And finally operators want to weigh the costs of – and footprint required to –utilize an optimization solution, which can be minimized by intelligently applying optimization on select network cells vs. on all traffic. What operators want is a balance, the ability to turn on optimization as needed.
How is such a balance achieved? In order to effectively reduce or limit congestion with optimization, it is necessary that optimization policies tie to overall congestion awareness, so the most appropriate optimization can be applied when and where needed, based on network conditions. Service providers can employ anticipatory and responsive techniques to deal with network congestion and ensure that optimization policies are designed with congestion in mind.
Available cell data can provide insights about known areas and times of congestion in advance to create time-of-day and location rules within the optimization system. In most mobile networks, congestion is highly predictable, occurring at the same locations at the same time periods and days of the week. In such a situation, applying optimization policies based on user identity, location and time of day can help to either minimize the impact on subscribers in known problem areas and times or avoid congestion altogether.
Responsive techniques can be utilized for either anticipated or unplanned congestion (perhaps due to technical issues or some sort of unplanned event that brings large numbers of users together). A responsive scenario requires the system to detect the network’s delivery ability in real time and respond appropriately with optimization techniques that are based on current congestion conditions and optionally to user identity and location. An additional benefit of responsive optimization is the ability to adapt the media delivery to a user with poor cell coverage rather than a poor connection due to congestion.
Different solutions offer different approaches to adding intelligence to optimization. At Vantrix we call this “Surgical Optimization.” What does that mean? It means pretty much what it says: giving service providers very precise control over how and when optimization is applied. Surgical Optimization is essential for service providers to help alleviate and better manage network congestion by delivering content more efficiently and with improved distribution of network bandwidth resources.
The Vantrix Bandwidth Optimizer provides both anticipatory and responsive Surgical Optimization techniques to assist service providers in dealing with network congestion and avoiding broad reductions in data usage. Vantrix does this within Bandwidth Optimizer by providing a comprehensive set of configurable optimization options and then matching this with granular policy control that allows a specific type of optimization to be enabled based on several different triggers. These triggers can be based on data collected within Bandwidth Optimizer, or can offer additional granularity through integration with external data (such as PCRF, LDAP databases, or RAN probes). These triggers can include:
- Device type
- Content type or source
- Network conditions
This offers service providers a comprehensive tool for managing optimization intelligently. At Vantrix, we’re innovating rapidly as this market need evolves. As we add more capabilities we will use this blog to provide more information on what we are up to. Stay tuned.
We hope you found this discussion on adding intelligence to optimization useful. In upcoming blog posts in this series, we’ll continue to define and discuss the Second Wave of Video Optimization and keep you informed about how Vantrix is working at the leading edge of optimization. For more information about the solutions available from Vantrix, please check out the solutions section of our website.