High-Performance SSP for Video Advertising Platform

About the Client

The client operates multiple ad-supported streaming channels across CTV and mobile, covering genres like documentaries, lifestyle, reality, and beauty shows. Their inventory runs continuous linear streams and on-demand video, generating tens of thousands of video ad views daily.

The video publisher relies on a legacy ad server that requires manual setup for each programmatic campaign. As traffic grows, it’s becoming harder to scale auctions, manage ad pods, and monitor performance across all channels, especially during peak load on top-performing shows.

Client Challenges

The client managed a mix of continuous linear streams and on-demand video, but their legacy system struggled to handle the growing programmatic traffic. Core issues included:

• High latency during peak traffic and large audience streams.
• Low fill rates and flat eCPMs across key placements.
• RPS that can’t scale at peak load, causing delivery gaps.
• Limited control over video advertising inventory.
• Narrow options to integrate additional new demand sources.
• Lack of real-time visibility into performance and revenue gains.
• Slow optimizations of ad delivery for popular shows with the largest viewership.

Technologies Used

Goals

The client’s goal was to optimize video ad operations and modernize their AdTech stack to manage growing traffic:

• Increase revenue from programmatic ad inventory.
• Manage ad pods, price floors, and inventory in one place.
• Integrate multiple programmatic partners to increase bid density.
• Track monetization trends and signals in real-time.
• Scale the platform to handle traffic spikes on top-performing shows.

Work Description

The client turned to Geomotiv to develop a solution tailored to their programmatic video advertising needs. After reviewing operational pain points, evaluating demand sources, and reviewing traffic volumes, we proposed a high-performance SSP for video advertising. To test this concept and ensure it aligned with the client’s goals, we first delivered a core MVP of the platform.

We broke the project into stages, prioritizing functionality that ensured high-throughput operations and gradual expansion based on traffic and demand. This approach let the team prioritize the most important functionality and expand the platform gradually:

Stage 1. Planning and analysis.

We audited the client’s infrastructure, identified bottlenecks and defined performance goals for the future SSP.

Stage 2. MVP design and architecture.

The development team designed modular architecture and identified core system components.

Stage 3. Iterative development of core modules.

Inventory manager.

First, we built the inventory manager as the base module and added internal APIs that unified the UI, backend, and inventory logic into a single system. With these capabilities, the video publisher could:

• Create and manage unlimited ad units.
• Set consistent pod structures and duration rules across all channels.
• Create rules to organize ad placements by category.
• Set base and dynamic floors.
• Unify all video ad placements in a single dashboard.

Then, APIs and dashboards were optimized to handle updates and queries across thousands of impressions per second.

OpenRTB endpoint.

The next step was to connect the solution with external DSPs. The team developed the RTB gateway that accepted real-time bid requests from programmatic advertiser partners. It mapped internal video inventory and impression metadata, including duration, ad formats, and floor prices, into OpenRTB bid requests for DSPs.

The endpoint executed the auction, selected the winning bid, generated a VAST tag, and served the video asset via a CDN to ensure fast playback during peak traffic. The system handled high throughput, allowing multiple DSPs to participate in auctions simultaneously without impacting performance.

Auction dashboard.

In this iteration, our team built an auction dashboard to let the video publisher oversee bids, fill rates, and revenue across all video inventory. The dashboard provided a single view of live auction activity and performance signals:

• win rates,
• timeout rates,
• bidder response latency.

As a result, the client could adjust ad pod configurations, set floors, and filter partners by analyzing real-time insights.

DSP integrations.

We added bidder adapters for all major programmatic buyers, including Magnite, Xandr, Smaato, Pubmatic, etc. The client could now send and process bid requests in real-time, monitor partner performance, and adjust priorities without performance issues. This setup helped increase competition for impressions and maximize yield across all video inventory.

Stage 4. Testing and optimization.

Our team ran load and performance tests on each module to maintain fast processing during peak traffic and ensure uninterrupted operations.

Stage 5. Release.

The developers deployed the SSP to production and continuously optimized performance metrics.

Stage 6. Enhancements.

With a stable, high-load architecture that handles large traffic volumes and scales across linear and on-demand channels, the SSP provides a strong foundation for future growth. The next steps on the roadmap will expand its capabilities:

• The platform will support header bidding, so multiple bidders could compete for the same impressions and increase publisher CPMs.
• It will support direct deals with premium advertisers, opening new opportunities for video ad monetization.

Results

The SSP for video advertising reliably handled peak traffic from day one. It helped increase fill rates by 18% and boost programmatic revenue by 22% within the first three months of the rollout.

It streamlines auction management, tracks performance in real time, and automatically adjusts ad delivery for peak traffic. The team continues to expand the platform: we add new features and scale capabilities so the SSP evolves with the client’s growing needs.