automation

"Championship Final",

A seller-side blueprint for using IAB Tech Lab’s LEAP APIs to tame CTV live concurrency. Practical architecture, code examples, and monetization playbooks.

"Championship Final",

Introduction: The Concurrency Crunch In CTV

Live CTV is finally mainstream, and it is unforgiving. Sports, news, awards, and tentpole premieres create massive, synchronized audiences that hammer ad systems simultaneously. When millions of viewers hit the same 30-second window, programmatic stacks buckle. On the sell side, we see it at the sharpest edge: QPS spikes pressure SSAI, ad servers, and SSPs all at once. Late ad decisions lead to slate. Buyers throttle to protect their spend or infrastructure, so bids disappear just when fill matters most. Forecasting is wrong by orders of magnitude. Campaigns under-deliver, and revenue gets left on the field. IAB Tech Lab’s Live Event Ad Playbook (LEAP) is a multi-phase effort to standardize the signals and workflows needed to make live ad delivery predictable and profitable. The first production deliverable, the Concurrent Streams API, gives a simple yet powerful building block: a standard, near-real-time answer to the question “how many viewers are in this live stream right now?” :cite[as8] The LEAP program then extends to forecasting, standardized prefetch, and creative readiness, building a practical ladder to stability and performance. This article is a seller-side guide. It distills the why, what, and how of implementing LEAP, with a focus on practical architecture, code samples, and yield tactics for publishers, SSAI vendors, and SSPs. We will keep it professional, slightly informal, and ruthlessly pragmatic.

What Is LEAP, And Why It Matters To Sellers

LEAP stands for Live Event Ad Playbook. It is a set of standards and APIs from IAB Tech Lab to make live streaming ad delivery interoperable and predictable across the buy and sell sides. The program’s stated phases include:

  • Concurrent Streams API: A standard way to share near-real-time live viewership counts
  • Forecasting API: Forward-looking live event projections to prepare capacity and demand
  • Standardized ad prefetch: Defuse QPS storms by getting decisions in advance
  • Creative readiness: Ensure creatives are approved and compatible before the moment

The “why” is straightforward. Live events can create sudden 10x to 100x spikes. Without standardized signals, everyone guesses. Buyers over-throttle or back out, publishers scramble with mitigation rules, and SSAI services absorb the blast radius. A small number of missing ad decisions compounds into whole-pod failures. LEAP’s first step, the Concurrent Streams API, creates a simple contract: a secure endpoint where authorized subscribers can retrieve current concurrent viewer counts for live events, optionally by region and insertion method. SSAI vendors are typically the data providers, and subscribers can be publishers, ad servers, SSPs, and DSPs. Buyers can use the signal to pace and scale. Sellers can use it to pre-scale infrastructure, set floors, gate demand intelligently, and communicate capacity and pricing to partners. :cite[as8,a31,cvs] Practical proof points are already in market discourse. IAB Tech Lab has advanced LEAP with industry stakeholders and positioned Concurrent Streams as the v1 standard to reduce missed ad breaks and guide scaling choices during live events. :cite[a31,cvs] This is the right spec at the right time.

LEAP Phase 1: Inside The Concurrent Streams API

At its core, the Concurrent Streams API is a lightweight schema and polling model. A provider hosts a secure endpoint. Authorized subscribers call it at a defined cadence and receive a snapshot of concurrent streams across live events. The spec emphasizes:

  • Current snapshot: Signaling the live edge, not batched analytics
  • Content identification: Map live events with AdCOM content fields to align with bidstream signals
  • Insertion type: Separate SSAI and CSAI counts where available
  • Regionalization: Coarse regions to guide scaling per data center
  • Decoupled transport: Out of band from OpenRTB to reduce per-request overhead

The schema includes an envelope with version and timestamp, an array of stream providers, and per-event media streams with streamcount entries that may break out SSAI and CSAI. :cite[as8] Here is a simplified example based on the specification:

{
"version": "1.0.0",
"timestamp": 1713366138000,
"streamsdata": [
{
"sdp": "Network A",
"mediastreams": [
{
"content": {
"id": "CMS123",
"title": "NBA Basketball: Lakers vs. Celtics",
"channel": {"name": "Sports Channel"},
"contentrating": "PG-13"
},
"eventstart": 1713366132000,
"eventend": 1713378132000,
"streamcount": [
{"region": 1, "sstreams": 140000, "cstreams": 400000},
{"region": 2, "sstreams": 100000, "cstreams": 20000}
]
}
]
}
]
}

A few seller notes:

  • Provider role: SSAI is often best-positioned to host this endpoint because it sees total live edge concurrency. Some publishers will host and aggregate across multiple SSAI partners.
  • Security: Use OAuth 2.0 or mTLS, and log per-subscriber usage. Rate limits and caching are your friends.
  • Cadence: 5 to 15 seconds is a common window that balances freshness against churn and overhead. Coordinate with buyers on expectations and SLAs.
  • Identity mapping: Ensure content IDs align with bidstream content objects for downstream actionability in SSP and DSP systems. OpenRTB 2.6 and AdCOM provide structures for CTV channel and podding signals you will want to line up with.

For exact fields and examples, see the public LEAP repository and the IAB Tech Lab site. :cite[as8,a31]

Architecture: Where The API Fits In A Seller Stack

Let’s break down a pragmatic seller architecture in four layers:

  • Event catalog and mapping: The publisher CMS and scheduler define events with IDs, channels, start and end times. Map to AdCOM content properties and to the ad server’s notion of placements and pods.
  • Concurrency observation: SSAI collects live edge metrics by event and region, possibly mixing device class signals and insertion type. This is your ground truth.
  • LEAP endpoint: A secure API service that normalizes the observation, publishes the snapshot schema, and enforces authorization, quotas, and caching.
  • Downstream consumers: Publisher ad server for capacity scaling, SSP for bidstream policy and floors, demand partners for pacing and budgets, and analytics for revenue ops verification.

In practice:

  • SSAI provider pushes metrics into a time series store, e.g., Prometheus or Cloud Monitoring.
  • LEAP service fronts the store with a consistent schema, caching the most recent snapshot per event.
  • SSP and ad server subscribe via an internal client, so they can adjust QPS budgets, deal prioritization, and floor strategies based on per-event concurrency.
  • Buyers subscribe for their own scaling and pacing. Shared signal reduces collective risk and avoids over-throttling.

The API is deliberately decoupled from OpenRTB so that it can be polled at a predictable cadence without bloating bid requests. :cite[as8]

Code: A Minimal Provider Endpoint

Below is a simplified Node.js Express service that returns a synthetic snapshot. In production, you would fetch from your SSAI metrics store, enforce OAuth, and add tighter caching and observability.

// server.js
import express from "express";
import crypto from "crypto";
const app = express();
const PORT = process.env.PORT || 8080;
// Replace with your metrics source
function getLiveStreamSnapshot() {
const now = Date.now();
return {
version: "1.0.0",
timestamp: now,
streamsdata: [
{
sdp: "PublisherCo",
mediastreams: [
{
content: {
id: "CMS-12345",
channel: { name: "Sports Prime" },
contentrating: "PG"
},
eventstart: now - 60 * 60 * 1000,
eventend: now + 2 * 60 * 60 * 1000,
streamcount: [
{ region: 1, sstreams: 125000, cstreams: 375000 },
{ region: 2, sstreams: 64000, cstreams: 98000 }
]
}
]
}
]
};
}
// Simple API key guard for illustration only
app.use((req, res, next) => {
const key = req.get("x-api-key");
if (!key || key !== process.env.LEAP_API_KEY) {
return res.status(401).json({ error: "Unauthorized" });
}
next();
});
app.get("/leap/v1/concurrent-streams", async (req, res) => {
const snapshot = getLiveStreamSnapshot();
// Weak ETag to enable caching on clients
const etag = crypto.createHash("sha1").update(JSON.stringify(snapshot)).digest("hex");
res.set("ETag", etag);
res.set("Cache-Control", "private, max-age=5"); // 5s TTL
res.json(snapshot);
});
app.listen(PORT, () => console.log(`LEAP provider running on :${PORT}`));

And a minimal client that your SSP or ad server can run as a subscriber:

# client.py
import os, time, requests
LEAP_URL = os.getenv("LEAP_URL")
API_KEY  = os.getenv("LEAP_API_KEY")
def fetch_snapshot():
headers = {"x-api-key": API_KEY}
r = requests.get(f"{LEAP_URL}/leap/v1/concurrent-streams", headers=headers, timeout=2.0)
r.raise_for_status()
return r.json()
def compute_qps_budget(event_streams):
total = 0
for sc in event_streams.get("streamcount", []):
total += int(sc.get("sstreams", 0)) + int(sc.get("cstreams", 0))
# Example: 0.8 requests per concurrent stream per minute
return int(0.8 * total / 60)
while True:
try:
snapshot = fetch_snapshot()
events = snapshot.get("streamsdata", [])[0].get("mediastreams", [])
for ev in events:
budget = compute_qps_budget(ev)
# Push budget to ad server or LB config
print(f"[{time.ctime()}] {ev['content']['title']} budget: {budget} rps")
except Exception as e:
print("Error:", e)
time.sleep(5)

These snippets are starting points. The real work is mapping your content metadata, securing the endpoint, and wiring the budgets into production traffic management.

Aligning With OpenRTB 2.6 And AdCOM For CTV

The LEAP Concurrent Streams signal sits alongside OpenRTB. It should not replace proper CTV signaling in bid requests. Use OpenRTB 2.6 and AdCOM to describe:

  • Video ad pods with slotting, sequence, and duration floors
  • Channel and network details in the content object
  • Durational floors for pricing longer ads

OpenRTB 2.6 formalized podding and added CTV-friendly improvements. Keep those fields accurate and consistent with your LEAP content IDs and titles so buyers recognize the event context. This alignment drives better deal mapping and fewer rejections. See IAB Tech Lab’s OpenRTB page and 2.6 highlights for the pod and channel guidance. A minimal 2.6 CTV pod example:

{
"id": "req-123",
"imp": [{
"id": "1",
"video": {
"w": 1920, "h": 1080,
"mimes": ["video/mp4", "application/vnd.apple.mpegurl"],
"plcmt": 1,
"minbitrate": 1200,
"maxbitrate": 8000
},
"pmp": { "deals": [{ "id": "deal-live-sports-123", "guaranteed": 0 }] },
"ext": {
"pod": {
"podid": "pod-1",
"adpoddurationsec": 120,
"minads": 2,
"maxads": 4
}
}
}],
"site": {
"id": "ctv-app-abc",
"name": "Sports Prime",
"domain": "sportsprime.example"
},
"device": { "ua": "Mozilla/5.0", "ifa": "ifa-123" },
"source": { "schain": { "ver": "1.0", "complete": 1, "nodes": [] } }
}

Your LEAP content.id and channel.name should match the data your buyers will see in reporting and your private marketplace deals. That continuity is where the monetization payoff shows up.

Seller Playbooks: Turning Concurrency Signals Into Revenue

Concurrency data by itself is a thermometer. The value comes when you plug it into decisions that either protect the experience or lift yield. Here are practical seller-side playbooks.

1) Pre-scale and dampen QPS spikes

Use concurrent streams to proactively scale SSAI decision nodes, ad server workers, and LB targets before the ad break. Aggressively pre-scale a few minutes ahead of kickoff, resume, or halftime.

  • Set thresholds: e.g., add one worker for every 25k incremental concurrent streams
  • Regionalize: Scale in the regions that the API shows as hot
  • Autoscale cooldowns: Avoid flapping by using hysteresis windows

Example pseudo-rule:

scalePolicy:
signal: leap.concurrent_streams.region[1].total
warmupSeconds: 120
thresholds:
- when: "value > 100000"  # 100k concurrents
addWorkers: 20
- when: "value > 250000"
addWorkers: 50

2) Protect fill by gating demand fairly

When concurrency spikes, buyers often self-throttle. Help them and yourself by enacting fair share gating for auctions.

  • Reserve RPS for top guaranteed or strategic deals
  • Apply back-off for high-error DSPs to preserve the pod
  • Expose a “recommended RPS” per buyer segment based on concurrency and pod timing

Fairness pseudocode:

def allocate_rps(concurrency, buyers):
base = int(concurrency * 0.02)  # 2% of concurrents per minute baseline
weights = normalize([b.priority for b in buyers])
return {b.id: max(50, int(base * weights[i])) for i,b in enumerate(buyers)}

Publish those RPS recommendations to buyers via your deal UI and, where feasible, via an API so their systems can adjust during the event. It reduces the guesswork that leads to misaligned throttling.

3) Dynamic floor prices with durational sensitivity

Live peaks are premium moments. Use concurrency to activate duration floors in OpenRTB 2.6 and adjust reserve pricing as concurrency rises, particularly for the first pod after key moments.

  • Floor curve: Floor CPM rises with concurrency, capped to avoid scaring bids
  • Duration multiplier: Longer ad slots demand higher per-second CPM
  • Contextual boosts: Apply increments for overtime, finals, or local-market games

Example floor function:

def durational_floor(concurrency, duration_sec):
base = 12.00  # base CPM
lift = min(8.00, 0.00005 * concurrency)  # +$0.00005 per concurrent, capped at +$8
per_sec = (base + lift) / 30.0
return round(per_sec * duration_sec, 2)

4) Communicate expected pressure to DSPs

Concurrency lets you preview pressure zones. Communicate two signals to buying partners:

  • “Next 10 minutes” peak bands: Light, moderate, heavy
  • Recommended creative weights: Prioritize shorter creatives during maximum load

This is advisory, not a hard block, but it aligns buyer pacing and creative choice with your real-time constraints and helps avoid timeouts.

5) Prefetch and ad pod pre-warming

Until standardized prefetch lands in LEAP, test prefetch patterns with trusted partners. Use concurrency to turn on prefetch selectively when peak thresholds are crossed.

  • Prefetch limited pods: Only the first slot or two, timeboxed
  • Verify VAST: Validate compatibility and CDN health ahead of playout
  • A/B test: Compare slate reduction and revenue against control

IAB Tech Lab calls out standardized prefetch as a future LEAP phase. :cite[a31] Start small now, and be ready to align when the spec lands.

6) Creative readiness as a first-class gate

Use concurrency to set stake-in-the-ground windows for creative review: the bigger the expected spike, the earlier you cut off unapproved creatives for that event.

  • Creative cutoffs: e.g., no new creatives inside T-30 for the biggest events
  • Ad Management API: Align with IAB Tech Lab creative approval flows where possible
  • Real-time fallback: Build safe default pods for demand that misses readiness

Buyer Experience: What Changes For Demand Partners

From the seller vantage point, improving the buyer experience is a revenue strategy. Concurrency signals help DSPs:

  • Scale infrastructure ahead of peaks and avoid timeouts
  • Pace budget to match real opportunity, not stale forecasts
  • Select creative lengths that minimize drop risk under load
  • Avoid false negatives where exchanges interpret timeouts as low interest

External coverage of LEAP emphasizes that publishers can send live viewership data to ad systems to reduce missed ad breaks, while buyers better handle spikes and adjust in real time. :cite[cvs] Your adoption story should center on “we are making it easier to buy our live events without sacrificing UX.”

Data Governance And Privacy-By-Design

Concurrency data is inherently aggregate. That is a feature. It is privacy-preserving and free of personal identifiers. To keep it that way:

  • No device-level export: Only aggregated counts per event and region
  • Coarse regionalization: Minimally necessary geographic resolution
  • Clear scope: The endpoint reflects live edge snapshots, not historical user analytics
  • Access control: Limit to trusted subscribers with purpose binding in your contracts

This approach aligns with privacy-by-design principles and reduces the compliance surface while still delivering the operational value.

Operational SLAs And Cadence

Concurrency snapshots must be fresh and predictable. Set explicit SLAs with subscribers:

  • Cadence: Every 5 to 10 seconds during live playout, slower outside live windows
  • Latency: Sub-500 ms endpoint response under peak load
  • Availability: 99.9 percent or better during live windows
  • Failover: Multi-region, multi-SSAI when applicable

Back these with traffic tests in staging that simulate 100x normal polling volume and use synthetic load right before major events to validate cache and LB behavior.

Testing: Minimal Experiments With Maximum Signal

Before going all-in, run a disciplined pilot on two live events:

  • Event A: Moderate size game to observe system behavior and calibrate budgets
  • Event B: High-stakes tentpole with buyer coordination and floor adjustments

Measure:

  • Slate reduction: Missed ad breaks and pod fill rate
  • DSP error rates: Timeouts, bid responses, and RPS acceptance
  • Revenue lift: eCPM deltas in high concurrency zones
  • Infrastructure cost: Scaling efficiency versus prior baselines

Two events are enough to draw directional conclusions and iterate. Document what you change for event B based on event A.

Common Pitfalls And How To Avoid Them

You will encounter snags. Anticipate them:

  • Unaligned content IDs: If LEAP content.id does not match your bidstream content, buyers cannot tie signals together. Fix mapping and keep it stable for the season.
  • Overly granular regions: Fine-grained geos add noise. Start coarse and only add regions if operationally meaningful.
  • Under-secured endpoints: A public endpoint will get scraped. Require auth, rotate keys, and rate limit.
  • Silent client failures: Put guardrails in your subscriber clients. If the API fails, fall back to conservative scaling and floors rather than drifting into chaos.
  • One-way communication: Concurrency helps most when both sides act. Share recommended RPS hints and incorporate buyer feedback loops.

Forecasting And Standardized Prefetch: Preparing For The Next LEAP Phases

While Concurrent Streams solves for “right now,” the LEAP roadmap includes:

  • Forecasting API: Share rolling two-to-three-week projections of event viewership to plan capacity and deals ahead of time
  • Standardized prefetch: Normalize how ad decisions are safely prefetched to reduce real-time pressure
  • Creative readiness: Codify approval and compatibility expectations

Industry reporting notes that LEAP’s toolkit is intended to help both sides prepare for surges, reduce missed ad breaks, and act in real time. :cite[cvs,a31] Sellers that start with Concurrent Streams will be best placed to snap in forecasting and prefetch when those specs mature.

How Red Volcano Can Help

Red Volcano specializes in publisher intelligence across web, apps, mobile SDKs, and CTV. Our value to sellers and SSPs sits in three lanes:

  • Discovery and benchmarking: Identify which publishers, SSAI vendors, and intermediaries are adopting LEAP and quantify coverage across channels
  • Live event catalog normalization: Map content entities across CMS, ad servers, and OpenRTB identifiers to reduce ID drift and improve buyer alignment
  • Signals strategy: Advise on how to integrate concurrency into floors, pod strategy, and buyer communications, combining ads.txt, sellers.json, and tech stack intelligence for a full-supply picture

Our north star is to help supply-side teams turn standards into revenue by aligning data, workflow, and partner execution.

Implementation Checklist For Sellers

Use this list to keep the rollout tight:

  • Decide provider model: SSAI hosted vs publisher hosted aggregation
  • Define content mapping: Content IDs synchronized across CMS, SSAI, ad server, and OpenRTB 2.6
  • Stand up the endpoint: Secure, cached, regionalized data returns within 500 ms
  • Write subscriber clients: Ad server and SSP clients to consume concurrency and adjust RPS floors and scaling
  • Set ops playbooks: Thresholds for scaling, floors, and buyer gating by concurrency bands
  • Pilot and measure: Two live events with clear KPIs, then iterate
  • Communicate to buyers: Share documentation, test windows, and recommended RPS behaviors
  • Postmortem and standardize: Roll insights into runbooks for the season

Frequently Asked Implementation Questions

Who should host the API in a complex supply chain?

If you are a publisher using one SSAI provider across your live events, have the SSAI vendor host. If you work across multiple SSAI vendors or have your own observability at the CDN edge, host an aggregation layer yourself and align secure access across partners.

How frequently should subscribers poll?

Five to ten seconds during live ad pods is a practical norm. Outside live windows, slow to 30 to 60 seconds to reduce overhead. Publish your cadence so buyers can plan.

Can we put concurrency in the bidstream instead?

LEAP is explicitly decoupled from OpenRTB to avoid inflating bid request payloads and to keep polling predictable. Keep using OpenRTB 2.6 for pod and content signaling. Use LEAP for out-of-band concurrency. :cite[as8]

How do we prevent gaming?

You control access and can watermark responses per subscriber. Return the same snapshot to each subscriber within a short TTL to avoid asymmetry. Keep regions coarse enough to prevent overly tactical behavior.

Will buyers actually use this?

Demand-side coverage suggests strong interest. Buyers want fewer timeouts, better pacing, and the ability to lean into high-impact moments. Shared standards reduce custom work. Industry coverage emphasizes that LEAP aims to create exactly that reliability. :cite[a31,cvs]

A Note On Standards And Market Momentum

IAB Tech Lab has positioned LEAP with visible industry collaboration. External reporting has cited involvement from major platforms and vendors, and the LEAP page describes the phased approach to concurrency, forecasting, prefetch, and creative readiness. :cite[a31] The Concurrent Streams API is published in the public repository, and the Tech Lab’s standards hub lists it as part of Supply Chain and Foundations. :cite[as8] That matters. Sellers do not want yet another custom endpoint per buyer. Buyers do not want to integrate different payloads per publisher. LEAP’s standardization is what makes the cost-benefit work.

Conclusion: Make Live Predictable

Live CTV is where brand spends are concentrating, and where user expectations are highest. Without shared signals, even great engineering teams get surprised. LEAP gives sellers a practical way to replace guesswork with coordination. Start with the Concurrent Streams API:

  • Publish a secure, fast snapshot of current concurrency
  • Align content IDs with your bidstream and deals
  • Plug the signal into scaling, floors, and buyer comms
  • Pilot, measure, and iterate across two events

Then be ready for forecasting, standardized prefetch, and creative readiness. The payoff is fewer missed ad breaks, better buyer trust, and higher yields at the moments that matter most. If you are a publisher, SSAI provider, or SSP looking to deploy LEAP effectively, Red Volcano can help you map your ecosystem, benchmark adoption, and turn signals into revenue outcomes. The future of live ad delivery is collaborative and standardized. LEAP is the step sellers can take today to get there.

References And Further Reading

  • IAB Tech Lab: Supply Chain and Foundations listings, including Concurrent Streams API entry and LEAP reference :cite[as8]
  • IAB Tech Lab: Live Event Ad Playbook announcement and context in industry coverage :cite[a31]
  • MediaPost coverage on LEAP goals and Concurrent Streams use cases :cite[cvs] For OpenRTB 2.6 CTV updates and podding guidance, see IAB Tech Lab’s OpenRTB resources and 2.6 documentation highlights.