2025 ProAV Trends: Hybrid Architectures and Software-Based AV Processors

The ProAV industry is no longer defined by rigid, hardware-only systems. In 2025, the shift toward hybrid architectures and software-defined AV processing is becoming a competitive necessity. Hardware remains critical — but the intelligence, routing, and signal manipulation are rapidly moving into software, edge processors, and virtual environments.

What’s driving this shift? And how can OEMs, system integrators, and product developers prepare their ProAV solutions to compete in this new landscape?

This article explores the technology trends behind this transformation, with real examples from the AV and broadcast industries.

What Are Hybrid Architectures in ProAV?

In traditional AV design, signal routing, encoding, and switching were handled entirely in hardware: matrix switchers, FPGA-based encoders, DSP units. But hybrid architectures combine:

Dedicated hardware blocks (e.g., FPGA for AV pipelines)
Software modules (e.g., signal routing, analytics, control)
Virtualized processing (e.g., encoding in containers, AV-as-a-Service)
Cloud integrations (e.g., asset management, live distribution)

This model decouples signal processing from physical constraints and unlocks new use cases:

Flexible scaling for multi-camera events
Remote diagnostics and orchestration
Easy firmware updates for performance improvements
Support for AI-based features like auto-framing, audio normalization

In short, hybrid means not “either-or”, but “best-of-both-worlds”.

The Rise of Software-Based AV Processing

A major shift in 2025 is the growing role of software-defined AV processors. These systems handle encoding, switching, and signal modification on general-purpose platforms, including:

x86-based servers
ARM-based SoCs
GPU/NPU-accelerated edge devices

Instead of hard-wired logic, signal processing is implemented via:

GStreamer pipelines
FFmpeg-based transcoders
WebRTC/SRT stacks
Custom containerized AV modules

These platforms bring a new level of flexibility:

Reconfigurable pipelines: change stream topology via config files or APIs
Remote control: full routing over REST or WebSocket
Hardware abstraction: same app runs on CPU, NPU, or GPU depending on the node

For example, a video conferencing hub built on ARM SoC can switch to higher-quality encoding or AI-powered audio cleanup through a software update — no hardware change required.

Why It Matters in 2025

AV projects today must meet faster timelines, changing specs, and smarter integration demands. A hybrid approach enables:

Faster prototyping with standard hardware
Integration with IP-based standards (ST 2110, IPMX, NDI)
Simplified product SKUs — one platform, multiple modes
Cloud orchestration for large venues, campuses, or remote production

OEMs benefit from:

Lower time-to-market
Simplified support & update cycle
Easier compliance with evolving protocols
More room to innovate through software features

This is especially true for startups and mid-sized vendors that can’t afford to develop a custom FPGA pipeline from scratch — they can now deliver ProAV features via software layers.

Real-World Example: Event Production Stack

Let’s consider a live production company that manages multi-camera coverage of conferences and hybrid events. In the past, they’d need:

A dedicated mixer/switcher
Multiple encoders for live/recording/monitor feeds
Hardware-based audio DSP

In 2025, they deploy a hybrid stack:

Raspberry Pi 5 or Jetson Orin boards at camera nodes
Local encoding and stream pre-processing in GStreamer
A central control system running on an x86 NUC, with cloud fallback
Remote UI for operator switching and stream labeling

When the client demands a different layout, resolution, or audio routing — it’s all configurable via JSON profiles or GUI. The team ships updates remotely.

AI Integration in Software AV Pipelines

One advantage of hybrid AV systems is native support for AI/ML. Rather than adding external hardware modules, AI is just another service:

Voice recognition for automated switching
Facial tracking for PTZ auto-control
Scene detection for dynamic layout generation
Real-time transcription or translation

These features are increasingly deployed via:

TensorFlow Lite or ONNX models on edge CPUs
CUDA or OpenCL models on GPUs
FPGA-accelerated logic on custom boards

AV processing pipelines are being augmented with AI steps — sometimes during ingest, sometimes during post-processing. The flexibility of software makes it easier to trial, iterate, and scale.

Challenges of Hybridization

Despite the benefits, hybrid AV architecture introduces new challenges:

Synchronization: keeping audio and video in perfect sync across software layers
Latency: software adds buffering and variability; needs mitigation via tuning
Standardization: interfacing proprietary software with industry AV protocols
Resource management: CPU/NPU/GPU workloads must be carefully scheduled

Designers must build in tools for monitoring, fallback, and thermal/power scaling.

Additionally, QA and regression testing become more complex. You’re testing not only hardware under load, but multiple versions of software stacks, AV chain configurations, and edge-case behaviors.

Planning a Hybrid ProAV Product

For product teams or OEMs looking to move toward hybrid AV:

Start with user expectations: What control, latency, and feature flexibility do your clients need?
Define processing topology: Which functions remain in hardware? What moves to software or the cloud?
Choose platforms wisely: x86 for power, ARM for efficiency, FPGA where determinism is needed
Architect for updates: OTA, container-based delivery, modular design
Design for diagnostics and metrics: remote logging, stream analytics, QoS visibility

At Promwad, we help clients bridge embedded engineering with scalable AV software design — whether that means building a Linux-based controller with FPGA video pipeline or creating a modular edge AI processing node with real-time video routing.