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Case Study • Media and Broadcasting

QA Engineering for a Broadcasting Company’s Split-Screen Mobile TV App

AccelOne led QA for a major broadcaster’s mobile TV app that runs live video and a game side by side in a split-screen view. AccelOne tested each system alone, then both together. The app launched without critical issues.

Media and Broadcasting
Engagement: QA Test Planning, Integration Testing, and Automated Regression Testing
Partnership: AccelOne QA Team Working Inside the Client’s Development Workflow
Smiling man in a living room holding a smartphone
2 Systems
Video and gaming tested together on one screen
6 Phases
From requirements to automated regression
Real Time
Bugs sent to developers as found
No Critical Issues
Stable playback and gaming at launch
At a Glance
~0
Manual tagging remaining
Near-complete elimination.
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Manual tagging remaining
Near-complete elimination.
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Manual tagging remaining
Near-complete elimination.
Overview
Broadcasting Company: mobile, QA, broadcasting

The client is a major broadcasting company. Its mobile app combines live or on-demand video with an interactive game in a single split-screen interface. Users watch and play at the same time, so both experiences have to hold up together.

AccelOne designed and executed the QA plan for the app. The team gathered requirements, built the QA infrastructure, ran component and integration testing, and worked directly with the client’s development team to resolve bugs before launch.

the challenge

When Execution Became Acceleration

Monetate initially engaged AccelOne to accelerate delivery across AI and UI initiatives. The scope was defined. The expectation was timely execution against an existing roadmap.

Onboarding moved quickly. Within two weeks, senior engineers were embedded across teams.

As integration began, something shifted.

Rather than simply delivering against the initially scoped plans, AccelOne engineers reviewed the architecture, sequencing, and assumptions behind the work. They asked practical questions, suggested alternatives, and identified ways to further streamline execution while improving long-term maintainability.

Austin describes it this way:

"Very quickly as they were onboarding, they surveyed our project plans and gave feedback that improved them.

Like if we do these things differently, we can get to market faster. Or if we take a different approach here, it will result in delivering higher quality software to your customers."

Austin describes it this way:

"Very quickly as they were onboarding, they surveyed our project plans and gave feedback that improved them.

Like if we do these things differently, we can get to market faster. Or if we take a different approach here, it will result in delivering higher quality software to your customers."

the challenge

Search and tagging had become a serious bottleneck

With millions of videos, search and tagging had become a serious bottleneck.

challenge 01

Manual tagging didn't scale

Description here.

challenge 01

Manual tagging didn't scale

Description here.

challenge 01

Manual tagging didn't scale

Description here.

challenge 01

Manual tagging didn't scale

Description here.

Highlights

Engineering Highlights

01

Integration Testing as the Critical Phase

AccelOne tested video streaming and gaming independently first, then ran both together across device types, network conditions, and sustained usage scenarios. Integration testing is where split-screen bugs surface, so the plan was built around it.

02

Bugs Reported in Real Time

Bugs were filed in RedMine and communicated to the client’s developers immediately. Fixes could be verified within the same testing cycle rather than the next one.

03

Built Around the Client’s Toolchain

RedMine was required by the client’s development workflow. AccelOne adapted to the client’s tools rather than imposing its own, so bug tracking fit how the developers already worked.

the solution

A hybrid AI video intelligence pipeline built for scale

Description here.

The Impact

Key Impact: Cost Reduction by Three Orders of Magnitude

One of the most significant achievements of this engagement was the reduction of AI processing costs by three orders of magnitude.

By combining on-premises transcription models with selective use of commercial APIs, AccelOne designed a hybrid architecture that:

This approach made the system not only scalable and secure but also economically viable at a massive scale, unlocking growth for Kurator without cost bottlenecks.

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The result was not just faster delivery, but stronger alignment around how

Monetate builds and evolves its platform.

Solutions

A Six-Phase QA Testing Plan for Concurrent Systems

AccelOne defined what needed to be tested before building the infrastructure to test it. Concurrent-system testing means validating each feature on its own, then validating all features running together while they share device resources.

Phase 01

Requirements and Scope Definition:

AccelOne mapped every testable component with the client: player functionality, game mechanics, split-screen behavior, audio handling, and device compatibility.

Requirements
Phase 02

QA Infrastructure Setup:

The team configured RedMine for bug tracking, JIRA for test case management, and Dropbox for file exchange with the development team.

Infrastructure
Phase 03

Isolated Component Testing:

Video streaming and gaming were tested independently first, so each system’s own functionality was validated before concurrent load was added.

Testing
Phase 04

Integration Testing:

Both systems ran together in the split-screen interface across device types, network conditions, and sustained usage scenarios. This is the phase where split-screen bugs surface.

Integration
Phase 05

Real-Time Diagnostics and Bug Resolution:

Bugs went into RedMine and straight to the client’s developers. This enabled rapid resolution instead of batch reporting at the end of each cycle.

Resolution
Phase 06

Automated Testing:

Automated regression scripts confirmed that a fix in one system did not introduce a new bug in the other.

Automated
Man on a couch at night looking at a smartphone app, lit by a blue screen
Results & impact

Real outcomes, measurable impact

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100×
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100×
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100×
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Under the hood

Open-source intelligence, cloud applied selectively

The pipeline combines open-source models with selective cloud services, running primarily on on-prem GPU infrastructure to deliver production-grade accuracy while avoiding cost, lock-in, and unpredictability.

Tech name

Description

Open Source

Tech name

Description

Open Source

Tech name

Description

Open Source

Tech name

Description

Open Source

This hybrid approach delivers production-grade accuracy while avoiding the cost, lock-in, and unpredictability of cloud-only architectures — making large-scale video intelligence economically sustainable at 2.5M+ video scale.

Project recap
AI-powered video intelligence at massive scale
the outcome

A searchable, time-addressable catalogue spanning millions of hours

What was previously locked behind sparse tagging and manual review is now discoverable at scale — while balancing cost efficiency with production-grade reliability.

before

Hours per batch of manual tagging

Hours per batch of manual tagging

Hours per batch of manual tagging

Hours per batch of manual tagging

after

Minutes of spot-check per batch

Minutes of spot-check per batch

Minutes of spot-check per batch

Minutes of spot-check per batch

The catalogue is now discoverable by:

Title

Description

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partners

Expanding the Partnership

As the early AI and UI initiatives gained traction, the collaboration expanded. AccelOne is now contributing not only to delivery, but to roadmap planning across AI, machine learning, and user experience initiatives.

Austin describes the ongoing dialogue as productive and forward-looking:

"As we work through these initial projects, having the teams weigh in on what the roadmap should look like next has been very productive. We've gained a great set of partners and feedback voices inside of AccelOne."

The partnership now operates at two levels:

  1. Accelerating current initiatives
  2. Accelerating current initiatives

For a platform operating at this level of complexity, that alignment matters.

output

A Model Built on Leverage and Trust

Monetate's goal was simple: Accelerate AI and UI initiatives without slowing the organization down.

What ultimately emerged was a partnership that increased leverage across the entire business.

Senior engineers were embedded quickly. Roadmaps moved faster. AI became part of daily workflows. Collaboration tightened across teams and time zones.

The engagement reduced friction and increased output.

And the relationship continues to expand, with AccelOne contributing to long-term direction beyond the initial delivery.

At this level of complexity, sustainable acceleration is not about adding more people. It's about embedding partners who raise the standard of how the work gets done.

The Outcome

Results & Impact

01
The app launched without critical issues.
02
Stable video playback confirmed across devices.
03
Gaming integration validated inside the split-screen interface.
04
Split-screen concurrent functionality verified.
05
Audio handling working correctly in both systems.
06
A high-quality user experience delivered at launch.
Impact

Business Impact

A clean launch is the primary measure of QA success. For a split-screen interface that combines two resource-intensive systems, that outcome required a QA methodology designed for concurrent-system testing, not one adapted from single-feature testing.

The client’s developers received bug reports as issues were found, and fixes were verified within the same testing cycle. When the app reached users, they could watch and play at the same time.

The Challenge

Two Demanding Systems Sharing One Screen

Most mobile QA engagements focus on one primary system: the stream in a streaming app, or the game in a gaming app. This app ran both at once, and QA had to prove the two systems worked together, not just independently.

Video decoding and game rendering both push mobile hardware hard. Running them together creates resource contention that only appears under concurrent load. Bugs that never show up in isolated testing can surface when both systems are active.

Where Split-Screen Video and Gaming Testing Gets Complex

Risk Why it matters in a split-screen app
Resource contention Video and game compete for the GPU, causing frame drops
Bandwidth sharing Shared network demand can make the video buffer
Audio conflicts Both audio streams must mix cleanly and still work independently
Synchronization issues If one system lags, the app must not freeze
Device variability Lower-end hardware can surface hidden issues
Thermal and battery impact Performance must hold over extended use
Technical Architecture

QA Toolchain and Video Streaming Test Coverage

AccelOne built the QA toolchain around the client’s existing development workflow.

Component Tool Role
Bug tracking RedMine Client’s required system for tracking and resolving bugs
Test case management JIRA Test cases, task tracking, and progress reporting
File exchange Dropbox Test assets, app builds, and documentation
Communication Skype and email Daily coordination and bug escalation

Every video streaming test also ran with the gaming system active.

Test area What was validated with the game running
Playback stability No interruptions under concurrent load
Network conditions Quality adapts on WiFi, 4G, and 3G
Resolution switching No artifacts or freezing
Buffering and recovery Clean recovery without affecting the game
Audio-video sync Audio stays in sync under gaming load
Cross-device compatibility Consistent quality across devices and OS versions
Strategic Value

Concurrent-System QA for Apps That Do Two Things at Once

This engagement shows when standard QA is not enough. Apps that run two resource-intensive systems at once need a test plan built around concurrent load. Testing each feature on its own misses the bugs that only appear when both compete for the same CPU, GPU, memory, and bandwidth.

Factor Single-feature testing Concurrent-system testing
Test scope Each feature alone Each feature alone, then together
Bugs found Misses concurrent-load bugs Catches concurrent-load bugs
Test conditions Short sessions Devices, networks, sustained use
Bug reporting Batched at cycle end Real time, fixed in the same cycle
Regression Checked within one feature Automated across both systems

Frequently asked questions

What QA services did AccelOne provide for the broadcasting company’s mobile TV app?
AccelOne designed and executed a complete QA testing plan for a split-screen mobile app that runs video streaming and gaming at the same time. The work covered requirements and scope definition, QA infrastructure setup, component testing, integration testing, real-time diagnostics and bug resolution, and automated regression testing. The app launched without critical issues.
What makes QA for a split-screen video and gaming app technically challenging?
Two high-demand systems share the same CPU, GPU, memory, and network bandwidth. QA has to confirm that each feature works on its own and that both work correctly together. That means testing for resource contention, bandwidth sharing, audio conflicts, synchronization issues, device variability, and thermal and battery impact over sustained use.
How does AccelOne approach QA for video streaming in a mobile app?
AccelOne’s video streaming QA covers playback stability, network conditions from strong WiFi to 3G, resolution switching, buffering and recovery, audio-video sync, and cross-device compatibility. For this app, AccelOne ran all of that testing while the gaming system was also active. This adds resource contention, which standard video QA does not account for.
What tools did AccelOne use for this mobile app QA engagement?
AccelOne used RedMine for bug tracking, JIRA for test case management and QA project oversight, Dropbox for file exchange, and Skype and email for real-time communication. RedMine was required by the client’s development workflow. AccelOne adapted to the client’s toolchain rather than imposing its own.
What results did the broadcasting company see?
The app launched without critical issues. Video playback was stable across devices, the gaming integration was validated, and split-screen concurrent functionality and audio handling were verified in both systems.
What should teams look for in a QA partner for an app with concurrent features?
Look for a partner that tests features in isolation before testing them together and treats integration testing as the critical phase. Based on this engagement, the practices that matter most are defining the full test scope before building test infrastructure, testing across devices, networks, and sustained use, reporting bugs to developers in real time, and automating regression so a fix in one system does not break another.
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