Midstream Content Selection in On-Demand Media Streams
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Solution Overview
Problem
On-demand video streaming platforms face inefficiencies in dynamically inserting content, such as advertisements or alternate media, into video-on-demand streams due to the need for pre-determined decisions before index file delivery, which can lead to increased latency and wasted effort, especially since viewers may not watch all cue points.
Innovation Solution
The system analyzes the index file for on-demand media streams to identify cue points and inserts virtual URIs along with a 'start decision process' marker, allowing for content selection and insertion decisions to be made shortly before the cue point, enabling server-side decision-making and adaptive content delivery based on client characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content selection decisions are made before index file delivery for on-demand streams, then all cue points can be prepared in advance, but infrastructure overload and increased TTFB occur
Solution Approach 1:
The system performs preliminary identification of cue points in the index file and prepares virtual URIs in advance, but delays actual content selection until midstream when the cue point is approached. This allows infrastructure preparation without forcing complete content selection before index delivery.
Solution Approach 2:
The system transitions from static pre-determined content selection to dynamic midstream selection. Content decisions are made flexibly based on real-time conditions such as whether the viewer is still watching, current network conditions, and client characteristics rather than being fixed in advance.
2Reliability
If content selection is made for all cue points before streaming, then content availability is ensured, but wasted processing occurs when viewers don't reach those cue points
Solution Approach 1:
The system performs only the necessary preliminary action of identifying cue points and creating virtual URIs, rather than completing full content selection for all cue points in advance. This partial preparation avoids wasted processing on cue points that viewers may never reach while ensuring content is ready when needed.
3Adaptability or versatility
If manual insertion of cue point markers is performed, then content insertion points can be defined, but the process becomes cumbersome
Solution Approach 1:
The system automatically identifies cue points in the index file using predefined criteria such as ad break markers or chapter transitions, eliminating the need for manual marker insertion. The index file structure itself provides the information needed to determine content insertion points.
4Productivity
If content selection is delayed until midstream, then processing efficiency improves, but real-time decision capability must be implemented
Solution Approach 1:
The system introduces a midstream content selection module that acts as an intermediary between the index file delivery and the actual content playback. This module monitors stream progression and triggers content selection at optimal moments, balancing processing efficiency with real-time decision capability.
Data Source
AI summary
An index file for an on-demand media stream (such as video-on-demand) is analyzed to determine one or more content insertion points, also referred to as cue points. The streaming platform modifies the index file such that the streaming platform can select the content to insert later on, preferably making that selection after the index file has been served and preferably a short time before the cue point in the stream. Put another way, the decision as to which content to insert at such points is not predetermined at the time of serving the index file. In this way, content insertions decisions can be made midstream.