Voices of Video

Swapping the engine while it runs sounds risky - and in production video systems, it is.
Hardware acceleration often gets positioned as “faster encoding,” but that’s not what makes engineering teams hesitate. The real challenge is introducing a new compute model without breaking workflows that already carry years of integrations, monitoring, and operational assumptions. As NAB approaches and the industry talks density and efficiency, the more important question is this: how do you evolve a platform without increasing fragility?
Leo Nieto from NETINT sits down with Dominique Vosters from Scalstrm to unpack what actually triggers the shift away from CPU-only scaling. The answer is less about technology hype and more about cost per stream across the full workflow, from transcoding through packaging and delivery. We explore why teams hesitate, what they are most concerned about breaking, and why live streaming raises the stakes compared to VOD.
We also get practical about architecture. Dominique explains how hardware acceleration fits into a broader system, where VPUs handle encoding while software layers manage resilience, synchronization, and recovery. From the outside, workflows often remain consistent. The real changes happen inside the system, where compute is relocated rather than the architecture being rebuilt.
Finally, we walk through a low-risk rollout approach: parallel environments, staging validation, and gradual channel-by-channel migration instead of big bang transitions.
If you are planning hardware-accelerated transcoding, VPU-based encoding, or broader video workflow optimization, this conversation will help you improve efficiency while protecting production stability.
Key topics covered
• why CPU-based video workflows become unsustainable primarily due to cost pressure across transcoding, packaging, and delivery
• why hesitation around hardware acceleration comes from fear of disrupting production systems, not skepticism about the technology
• differences between VOD and live streaming workflows, and why live environments require more cautious rollout strategies
• how hardware acceleration fits into existing architectures, with software layers handling resilience, synchronization, and recovery
• what actually changes when acceleration is introduced versus what remains stable for operators and workflows
• the concept of “swapping the engine while it runs” by keeping input and output behavior consistent while moving compute inside the system
• when orchestration layers add value and how that depends on platform maturity and scale
• practical deployment strategies including parallel environments, staging validation, and gradual channel-by-channel migration
Stay tuned for more in-depth insights on video technology, trends, and practical applications. Subscribe to Voices of Video: Inside the Tech for exclusive, hands-on knowledge from the experts. For more resources, visit Voices of Video.

The hardest part of streaming isn’t picking a codec or buying faster hardware. It’s making the whole system work together.
In this episode of Voices of Video, Mark Donnigan from NETINT sits down with Joe Waltzer, CEO of Arcadian, to break down the real anatomy of a modern video streaming platform and why teams so often stall at the last mile between a working demo and a production-ready service.
They walk through the core building blocks behind real-world workflows: CMS for metadata and app experience, DAM or MAM for managing assets, transcoding pipelines for device-ready outputs, and distribution systems spanning apps, APIs, and CDNs. Joe also highlights the often-overlooked avails and compliance layer, from licensing windows to geo blocking, and why mistakes here can turn into real business risk.
The conversation then zooms out to what matters today: cost pressure, practical uses of AI, and the push to bring services built for North America and Europe to underserved global markets.
They also dig into quality of experience. How studios define quality, why “good 720p” is real, and why viewers instantly feel issues like bad lip sync or subtitle timing even if they cannot explain them.
If you are building video pipelines, streaming apps, or end-to-end media systems, this episode gives you a clear view of what actually has to connect for quality, scale, and profitability.
Key topics:
• The five core systems: CMS, DAM or MAM, transcoding, distribution, and avails
 • Why “drop-in solutions” rarely exist in streaming
 • How Arcadian helps teams finish the last 5% to production
 • Integration vs building new platforms
 • Real-world operations: QC, audio sync, subtitles, edge cases
 • Cost reduction and where AI actually fits today
 • Scaling to underserved markets through efficiency
 • Why distribution matters more than “content is king”
 • Cloud gaming and the shift of bottlenecks to encoding
 • Studio quality standards and what viewers actually notice
Interested in joining Voices of Video or sharing what you're building?
Reach out here: https://netint.com/voices-of-video/
Stay tuned for more in-depth insights on video technology, trends, and practical applications. Subscribe to Voices of Video: Inside the Tech for exclusive, hands-on knowledge from the experts. For more resources, visit Voices of Video.

The fastest benchmark number is comforting right up until your video system meets the real world.
Staging looks stable. Charts look clean. Then production introduces variability at scale: messy networks, mixed content, thermal constraints, I/O bottlenecks, memory pressure, and timing issues that never show up in controlled tests. That’s where “efficient” can suddenly mean fragile. 
In this episode of Voices of Video, we sit down with Juan Casal, Partner and Chief R&D Officer at Cires21, alongside Leonardo Nieto from NETINT, to unpack what actually changes when systems move from lab results to real production environments.
We explore why evaluating isolated components such as encoder throughput, codec efficiency, or cost per stream can be misleading, and why the full video pipeline must be measured as a balanced system. Local optimization often shifts pressure downstream, turning encoding gains into decoder frame drops or network bursts that degrade quality of experience.
The core theme is operational confidence.
Predictability beats peak performance. Stable latency, stable resource usage, and clear worst-case behavior are what make capacity planning possible at scale. We also dive into observability and metrics that matter in production: variance vs averages, jitter, timestamp alignment, and how to design systems so failures can be predicted instead of simply reacted to.
Efficiency, in practice, is headroom. It is the margin that allows your infrastructure to absorb variability, whether you are running on CPUs, GPUs, or purpose-built accelerators.
If you are building or upgrading video encoding, transcoding, or streaming infrastructure ahead of NAB, this conversation will challenge how you evaluate performance.
Join the conversation:
https://voicesofvideo.netint.com/join-the-conversation
Learn more about:
Cires21 → https://cires21.com
NETINT → https://netint.com
Key topics covered:
• Why production introduces variability through scale, system interaction, and constraints like thermal limits and I/O bottlenecks
 • Why end-to-end pipeline balance matters more than single-component optimization
 • How local optimizations shift load downstream to decoders and networks
 • Predictability as the real operational goal: stable latency and resource usage
 • Why worst-case behavior and variance matter more than peak throughput
 • Observability gaps: jitter, timestamp alignment, and network bottlenecks
 • Real-world failure modes such as clock accuracy and jitter causing frame drops
 • Efficiency as headroom: stability, burst tolerance, and higher saturation thresholds
 • Designing for failure prediction and testing under sustained load
Stay tuned for more in-depth insights on video technology, trends, and practical applications. Subscribe to Voices of Video: Inside the Tech for exclusive, hands-on knowledge from the experts. For more resources, visit Voices of Video.

“Ramageddon” is what happens when memory pricing goes vertical and suddenly your infrastructure plans no longer pencil out.
In this episode of Voices of Video, we talk with Dell Technologies about why video infrastructure is being rebuilt around power limits, cooling realities, and purpose-built silicon like NETINT VPUs.
As video becomes the dominant data type and the foundation for AI workloads, the constraints are no longer theoretical. Power, density, and supply chain volatility are now first-order design inputs.
We dig into what it actually takes to deploy and manage video systems at a global scale without losing control of cost, security, or uptime.
• Dell’s current focus across AI and video workloads
• Why density and energy efficiency now drive infrastructure decisions
• GPUs versus VPUs and the case for purpose-built video acceleration
• Data center power and cooling options, including air and liquid cooling
• “Ramageddon” and broader component shortages affecting buyers
• How Dell uses supply chain scale and configurability to deliver systems
• Managing large fleets with deployment services, support, and automation
• Firmware updates as a pathway to real encoder improvements over time
• Supply chain security and root of trust at massive scale
• Edge computing growth across far edge, near edge, and metro
If you are building, scaling, or rethinking video infrastructure, this is a grounded, operator-level discussion of where things are actually headed.
Learn more about NETINT Technologies and purpose-built video processing, or explore enterprise infrastructure from Dell Technologies.
Stay tuned for more in-depth insights on video technology, trends, and practical applications. Subscribe to Voices of Video: Inside the Tech for exclusive, hands-on knowledge from the experts. For more resources, visit Voices of Video.

Your neighbor cheered before your stream - now what?
In this episode of Voices of Video, we move past the generic advice to “make it faster” and dig into why latency has become a structural constraint in modern video systems. It’s no longer just a performance metric. It dictates where compute lives, how encoding is deployed, how traffic is routed, and what it really takes to deliver reliable, real-time video at scale.
With i3D.net co-founder Stefan Edeler, we unpack the architectural decisions that separate stable platforms from fragile ones.
We start with the workload lens. Live sports, betting, auctions, and interactive formats cannot hide behind buffers. When user actions, commentary, or creator feedback loops back into the stream, encoding can’t sit in a distant region. It must move closer to viewers, often across multiple sites. That shift forces teams to balance geographic distribution against blast radius, cost, and the very real operational load of running many locations.
One key insight: pretty ping times don’t equal quality. What matters is sustained throughput into last-mile ISPs during peak hours, packet loss behavior, jitter, and whether your providers truly have capacity headroom when it counts.
From there, we zoom out to platform strategy. Cloud accelerates early builds, but egress-heavy video workloads can quietly crush budgets. Hybrid models and bare metal often win on cost and control, yet introduce vendor sprawl and operational complexity.
Stefan outlines a pragmatic path forward:
Prototype quickly in the cloud across a few regions. Validate failover and backhaul. Then expand step by step into the right geographies with strong peering and measurable demand.
Instrument everything - edge QoE, per-ISP performance, jitter, exit rates - and let real telemetry guide routing decisions and site expansion. The objective is not just scale, but resilience: a self-healing system that detects carrier trouble and automatically shifts users onto healthier paths.
We close with practical guardrails to avoid over-engineering:
Optimize until users can no longer perceive improvement. Choose five excellent sites over thirty fragile ones. Challenge every “we must” with observability data. And recognize that legal, licensing, and sovereignty requirements now shape placement decisions as much as physics does.
The takeaway is clear: architectural intent beats component speed.
If you’re wrestling with latency budgets, interactive encoding, or the cost of scaling globally, this conversation offers a blueprint for making durable, data-driven choices.
In this episode, we cover:
Defining latency budgets and aligning them with architectural intent
Mapping workloads to geography and last-mile ISP realities
Measuring sustained throughput, packet loss, and capacity, not just RTT
Deciding when to distribute compute and how far to push it
Placing encoding for interactive and feedback-driven streaming
Balancing resilience against operational overhead
Expanding stepwise with strong observability and real telemetry
Navigating cost trade-offs: cloud egress vs. hybrid and bare metal
Designing for automated failover and self-healing routing
Accounting for legal, licensing, and data sovereignty constraints
If you’re coming to NAB Show, come talk with our team at NETINT and explore
Stay tuned for more in-depth insights on video technology, trends, and practical applications. Subscribe to Voices of Video: Inside the Tech for exclusive, hands-on knowledge from the experts. For more resources, visit Voices of Video.