What is Edge Caching?

Video streams break into small segments, often two seconds long. When a viewer starts playback, the player requests these segments from the nearest edge node. If the node has the segment, it serves it immediately. This is a cache hit. If not, the node fetches the segment from the origin server, stores a copy, and then serves it to the viewer. This is a cache miss.

The effectiveness of this system depends on two main factors. First is the cache hit ratio, which measures the percentage of requests served from the cache rather than the origin. A higher ratio means less traffic hitting your origin server. Second is the Time-to-Live (TTL) setting. TTL defines how long a cached segment remains valid before the edge node must re-fetch it from the origin. If you change a video’s metadata or replace a segment, you must update the TTL or purge the cache to make sure viewers see the new content.

For live streams, caching works differently. Live segments are only cached for a very short window, usually just one or two segment durations. This keeps viewers get the most recent data. For Video on Demand (VOD), segments can be cached for hours or days, since the content does not change.

Without edge caching, every viewer request travels all the way to your origin server. This creates three problems. First, latency increases because data travels longer distances. Second, your origin server becomes a bottleneck during peak viewing times, potentially causing crashes or slow responses. Third, you pay higher egress fees for transferring large amounts of data from your origin to the internet.

Edge caching solves these issues by distributing the load. When thousands of viewers watch the same popular title, the edge nodes handle the majority of the traffic. Your origin server only serves the initial requests that populate the caches. This keeps your infrastructure stable and your costs predictable.

For operators, this translates to better user experience and lower operational overhead. Viewers experience faster start times and fewer buffering events. You spend less on bandwidth and server capacity. It is a fundamental part of scalable video delivery. Most mid-roll complaints trace back to bad cue points, not the ad server, but delivery issues like high latency can also drive churn if not managed properly.

• Ignoring TTL settings: If you update a video file but do not adjust the TTL or purge the cache, viewers may see outdated content. Always verify cache invalidation procedures.
• Caching dynamic content: Do not cache personalized or ad-inserted segments if they vary by user. This leads to incorrect content delivery. Only cache static, universal segments.
• Underestimating cache size: If your edge nodes have small storage limits, they may evict popular content too quickly, lowering the hit ratio. Monitor cache performance regularly.
• Assuming one-size-fits-all: Live and VOD content require different caching strategies. Applying VOD TTLs to live streams causes significant latency.