When your OTT platform experiences a surge of viewers — say a live sports final or a highly anticipated premiere — you might notice something alarming: your bandwidth usage and delivery costs skyrocket. The likely culprit is unicast streaming. That’s where the multicast vs. unicast debate starts to matter: multicast, while more complex to implement, can deliver the same stream to many viewers simultaneously, making it significantly more efficient during high-traffic moments.
In the article below, there is a strategic analysis of how unicast streaming can lead to unexpected costs and performance issues during peak-viewership events on OTT platforms.
Unicast Streaming and Hidden Cost Drivers
Unicast is simple and widely supported, but it doesn’t scale efficiently. Each additional viewer requires a separate stream, so serving 100,000 concurrent viewers means 100,000 duplicate streams leaving your servers or CDN edge locations. This one-stream-per-user model multiplies bandwidth usage and server load for every new viewer. In other words, in unicast delivery, your distribution costs increase roughly linearly with audience size, which can lead to eye-watering bills at scale.
Consider what happens during a major live event: every viewer is requesting the same content at the same time. Unicast will deliver that same piece of content over and over to each person individually. Not only does this inflate your bandwidth, but it also strains network capacity. High traffic demand under unicast can cause network congestion, leading to latency or buffering issues. Those performance hiccups are unacceptable in high-stakes live events like sports, where viewers expect real-time, high-quality streams.
Live Events: When Unicast Inefficiency Peaks
For live events with massive concurrent audiences, unicast can become a bottleneck. A dramatic example was a recent boxing match stream that reportedly drew 65 million concurrent viewers — a scale that pushed conventional CDN infrastructure to its limits. At that scale, the unicast nature of streaming revealed its downside: standard content delivery networks weren’t designed for millions of simultaneous one-to-one connections.
Even if your events are smaller, the principle holds. Live sports, concerts, and breaking news often create synchronized viewing spikes. Under a unicast model, 500,000 viewers equate to 500,000 outgoing streams.
If each stream is 5 Mbps of HD video, that’s an enormous 2.5 Tbps of outbound traffic.
Delivery costs at this scale can be staggering, especially when you’re paying per gigabyte.
AVOD Platforms: Thin Margins and High Volume
In the AVOD model, users don’t pay for content; revenue comes from advertising. This means each viewer might only generate a few cents in ad revenue per stream. If delivering that stream via unicast costs a similar few cents in bandwidth, your profit margin per user shrinks dramatically.
For example, an hour of HD streaming can consume several gigabytes of data. If your CDN charges even $0.05 per GB, a single viewer-hour might cost you around $0.10 to deliver. Now, imagine a hundred thousand concurrent AVOD viewers each watching free content. The delivery costs could rapidly approach or exceed the ad income from those viewers.
Moreover, ad-supported services thrive on volume: the more streams you serve, the more ads you can show. But with unicast, serving more streams drives costs up one-for-one, so scaling up viewership doesn’t scale profit in the same way.
Using CDN Network Solutions to Mitigate Unicast Costs
A robust CDN caches content on distributed servers closer to viewers, which means during popular events, your origin server isn’t uploading the same stream millions of times. Instead, the CDN edge can serve many local viewers with cached copies. This reduces redundant data transfer across the core network and lowers your transit burden.
Here are a few strategies to consider:
- Leverage multi-tier and private CDNs: Public CDNs spread content globally but can come with high transfer fees. Some operators opt for internal or private CDNs within their own network or ISP partnerships to cut down third-party transit costs. Large streaming services like Netflix deploy their own CDN appliances inside ISP networks, saving everyone money by serving content locally, and it saves ISPs hundreds of millions annually by reducing external traffic. Smaller OTT providers can collaborate with CDN providers or ISPs for similar, albeit smaller-scale, cost benefits.
- Optimize for high-traffic events: Plan capacity with headroom and use event-specific CDN routing or overflow strategies. Some platforms pre-position content on edge servers and use load balancing across multiple CDNs to ensure no single unicast pipeline gets overwhelmed.
- Explore multicast and peer-assisted delivery: Traditional IP multicast isn’t widely available on the open internet, but new approaches like multicast ABR (Adaptive Bitrate) and peer-assisted streaming are emerging. In controlled trials, combining multicast distribution with open caching cut peak traffic – a huge efficiency gain that directly translates to cost savings. Likewise, peer-to-peer CDN extensions (where users’ devices share some data among each other) can offload origin and edge servers.
- Improve video encoding efficiency: While not a networking solution per se, using modern codecs and adaptive bitrate ladders can reduce the size of each stream. Smaller stream sizes mean less data per viewer. Over millions of views, even a 10–20% bitrate reduction can save a significant amount in delivery costs. This complements your CDN strategy by ensuring you’re not sending a single byte more than necessary for acceptable quality.
Conclusion
High traffic should be a cause for celebration (more viewers!) rather than a panic about infrastructure and expenses. By planning for scalability beyond basic unicast, OTT platforms can serve huge audiences and keep costs under control, all while delivering the smooth streams that viewers expect. In the competitive streaming market, efficiency and reliability are not just a technical win, but a business one.
