broadpeak.io leverages the content replacement technology to enable Virtual Channel scenarios. A virtual timeline for each personalized subscriber can be built through API, through which VOD(s) or live Event(s) can be scheduled as a replacement content, over a period of time. It relies on an existing live channel used as a base stream, on which content replacement is applied. A Mediapoint (ESNI API), or a content replacement slot (Rest API) allows to define when and for how long to apply the content replacement, the replacement Source, and the specified Audience it applies to.
A typical ecosystem to implement Virtual Channels is the following:
The most basic approach to Virtual Channels is to consecutively play Video-on-Demand assets to the TV viewer. Typical Virtual Channels would revolve around specific themes such as Cooking, Travel, Nature, Hunting, etc. Most advanced use-cases also allow the creation of Virtual Channels based on a combination of VOD and Live program or events. broadpeak.io allows you to perform these uses-cases.
Taking advantage of the server-side technology, broadpeak.io implements the Virtual Channel logic seamlessly in the back-end, so everything is abstracted to the client. When creating a modern Virtual Channel on broadpeak.io, a Live manifest is delivered to TV viewers, so the player sees a real Live stream.
Creating a Virtual Channel on broadpeak.io implies that an external system is able to perform a scheduling, selecting what are the VOD(s), or Event(s) that need to be be scheduled, at what time, and for how long. It is usually represented by what we call a Virtual Timeline, which is the graphic representation of the consecutive programs scheduled on the channel over time. For each program that needs to be scheduled, a content replacement slot must be created through API for your Virtual Timeline to be effective as a Virtual Channel. broadpeak.io being an API platform, it does not provide a graphic display of the scheduled slots over time, but this could look like the one below:
When working HLS and DASH, the same Virtual Timeline must be built over both HLS and MPEG-DASH Services to ensure that all types of devices will get the same experience. Please note that due to the nature of the standards, the splicing precision is HLS, which usually relies on larger segment size, is lower than in MPEG-DASH. For more information, please see the section Understanding broadpeak.io behavior .
A better way to engage with an audience can be achieved through the use of Personalized Virtual Channel. With technologies such as recommendation services, which are made to provide a list of content that matches the preferences of the TV subscribers, it is possible to build a channel entirely based on the content that TV subscribers love and expect of a lean back experience.
A typical ecosystem to implement Personalized Virtual Channels could look like the following:
broadpeak.io's content replacement technology supports the creation of replacement slots based on Audience(s). An Audience can be a group of TV subscribers, devices, a category of users or even a list of zipcode that represents a region. Different level of granularity can be achieved, the smallest being a single TV subscriber, or device.
It is possible to create a replacement slot for a specific Audience through the ESNI or REST API, so that personalization is only applied when that specific Audience requests the content. In that case, the manifest delivered references a personalized content. Upon streaming request, the Audience is identified through a query parameter which can either be "category" or "zipcode" depending on how the Audience has been built. You can find more information about defining the Audience in the Handling Blackout slots section.
The same way as it is done for the Virtual Channel scenario, each Virtual Channel variant needs to be scheduled accordingly based on the program that needs to be aired, the program's start time and its duration. A Virtual Timeline for a personalized channel would look like this, in the case of a TV subscriber base segmented into two Audiences.
Updated 3 months ago