Deep Dive: Netcode Optimization with Fine-Grained Visibility and Multi-Packets

Creating a smooth multiplayer experience is one of the hardest challenges in game development. As player counts rise and gameplay complexity grows, the bandwidth requirements for state synchronization can skyrocket. Recently, the technical series over at Wirepair.org explored cutting-edge methods to solve these problems.

In this article, we are breaking down the concepts of Fine-Grained Visibility and Multi-Packet optimization to help you understand how modern netcode reduces latency and bandwidth usage.

The Problem: Bandwidth and the "N²" Issue

In a naive multiplayer implementation, the server sends the state of every entity to every client. If you have 10 players and 100 NPCs, that is manageable. But what happens when you have 100 players and 5,000 entities?

Sending updates for 5,000 objects to 100 clients results in a massive data flood. Most of this data is wasted because a client cannot possibly interact with or see entities on the other side of the map. This is where optimization becomes critical.

Part 1: Fine-Grained Visibility

The first pillar of optimization discussed is Fine-Grained Visibility. While basic "distance culling" (hiding objects far away) is standard, fine-grained visibility takes this much further to ensure clients only receive data they actually need.

Interest Management

Fine-grained visibility relies on robust Interest Management. The server categorizes what a client "cares about" based on several factors:

• Spatial Partitioning: Using data structures like Octrees or BSP trees to quickly determine which entities are near a player.
• Line of Sight (Raycasting):strong> Even if an enemy is close, if they are behind a thick wall, do they need to be updated? Fine-grained systems often cull entities that are occluded to save bandwidth on animation states and position updates.
• Gameplay Relevance: A player driving a car doesn't need the intricate physics data of a boat on the other side of the river. Prioritizing updates based on what impacts the player immediately is key.

By implementing these checks, the server stops sending "noise" to the client. This reduces the CPU load on the server (less serialization) and the bandwidth load on the client.

The "Multi-Packet" Approach

The second half of the first optimization strategy involves how data is physically sent over the wire.

Standard networking often involves sending a separate packet for every event or update. However, UDP/TCP headers have overhead. Sending 100 small packets is inefficient due to header bloat.

Multi-Packets (or Packet Coalescing) solve this by bundling multiple entity updates into a single network packet.

• Reliability: Instead of acking 50 small packets, the client acks one large bundle.
• Throughput: You maximize the usage of the MTU (Maximum Transmission Unit), filling the packet to the brim with useful data rather than headers.
• Prioritization: Critical updates (like shots fired) can be sent immediately in their own packet, while mundane updates (like a grass swaying) can be bundled into the next multi-packet cycle.

Part 2: Advanced Optimization Strategies

Once visibility and packet bundling are in place, the next step is optimizing the data inside those packets. The second part of the optimization series focuses on squeezing every bit of performance out of the data stream.

Delta Compression

Sending the full state of an object (X, Y, Z, Rotation, Velocity) 60 times a second is redundant. Delta compression involves only sending what changed since the last update.

• If a player is standing still, send zero bytes for position.
• If a player is moving in a straight line, perhaps only send a velocity vector and let the client extrapolate.
• This drastically reduces the average packet size, allowing you to fit more entity updates into a single multi-packet.

Quantization

Network engineers often use Quantization to reduce data size. Instead of sending a 32-bit float (4 bytes) for a position coordinate, you might send a 16-bit integer (2 bytes).

By sacrificing a tiny bit of precision—which is unnoticeable in a fast-paced game—you can cut your bandwidth usage in half for positional data. Combined with fine-grained visibility, this ensures that the limited bandwidth you have is used for maximum impact.

Packet Prioritization Queue

Not all packets are created equal. Advanced netcode implementations utilize a priority queue:

1. High Priority: Player input, weapon shots, player deaths.
2. Medium Priority: Player movement, nearby NPCs.
3. Low Priority: Ambient effects, weather changes, distant chat messages.

When the network buffer is full, the system drops low-priority packets first to ensure gameplay mechanics remain responsive.

Conclusion

Optimizing netcode is a balancing act between precision, bandwidth, and CPU overhead. By adopting Fine-Grained Visibility, developers ensure clients aren't overwhelmed with irrelevant data. By utilizing Multi-Packets and Delta Compression, they ensure the network is used efficiently.