The Need for Compression in Global Network Traffic

Introduction: Why Compression Has Become Strategic Again

In the age of 4K/8K streaming, cloud gaming, distributed artificial intelligence and the Internet of Things (IoT), global networks carry exponentially growing volumes of data. Despite the continuous expansion of fiber-optic link capacity and the rollout of 5G, demand often outstrips available capacity at critical points: intercontinental links, national backbones and mobile network segments. Data stream compression is therefore re-emerging as an essential means of increasing usable capacity, reducing infrastructure costs, limiting latency and shrinking the energy footprint.

The Current State of the Global Network

The global network relies on a complex web of submarine cables, terrestrial backbones, Internet exchange points (IXPs) and wireless access networks. Transoceanic cables now offer speeds on the order of several terabits per second per cable, and operators are investing heavily in redundant routes. Bottlenecks, however, often occur at three levels:

• Local and mobile access links (the last mile), where capacity is shared and variable;
• Internet exchange points and core routers, where concentrated traffic causes intermittent congestion;
• Operating and energy costs, which rise with the amount of data processed and stored.

As a result, operators and content providers are seeking solutions that complement physical bandwidth expansion: routing optimization, caching, adaptive encoding and compression. These approaches improve efficiency without waiting years for new infrastructure.

Compression: Types and Benefits

Compression can be applied at several levels of the network model:

• At the application level (video, audio and images), using improved codecs such as AV1, VVC/HEVC or proprietary codecs;
• At the transport or stream level (packet compression and deduplication at the TCP/QUIC level);
• At the storage and encrypted transmission level (compression before encryption and research into homomorphic compression);
• At the edge and IoT level (source-side compression, data aggregation and lightweight AI models for preprocessing).

The benefits are tangible: lower perceived latency, reduced transit costs, lower storage requirements and more resilient services in areas with limited connectivity. Environmentally, transferring less data means less energy consumed by data centers and network equipment.

Technological Advances and Research Needs

Progress in compression concerns not only the efficiency of bitrate reduction but also processing speed (low-latency compression and decompression), resilience to data loss, compatibility with encryption and the ability to adapt dynamically to network conditions. Priority research areas include:

• Adaptive low-bitrate codecs for video and voice, capable of optimizing perceived quality according to available bandwidth;
• Lossless and near-lossless compression algorithms for machine data (telemetry and logs) that preserve useful information while substantially reducing data volumes;
• Compression managed at the transport level (e.g., integration into QUIC/HTTP3) to reduce the overhead of modern protocols;
• Approaches combining AI and compression (autoencoders, quantization and sparsity) for additional gains, particularly in multimedia streams and distributed machine learning models.

China’s Push: Where Things Stand

China has made mastery of infrastructure and transmission technologies a strategic objective. Its priorities encompass the large-scale rollout of 5G, the expansion of submarine cable routes and sustained research and development in compression and network optimization technologies. A few observations:

• Chinese companies, including equipment suppliers, cloud operators and over-the-top (OTT) providers, are investing in proprietary codecs and streaming optimization solutions tailored to local needs;
• National programs support research into AI applied to compression, edge computing and hardware-algorithm co-design to accelerate decompression with low power consumption;
• Large-scale deployments of peering infrastructure, caches and national content delivery networks (CDNs) reduce dependence on international links and increase pressure on compression techniques to optimize the remaining cross-border traffic.

These efforts show that China is not merely increasing raw capacity: it is investing to make transmission more efficient and less dependent on continuous physical infrastructure investment.

Constraints and Challenges

Developing high-performance compression methods requires overcoming several constraints:

• Latency: computationally intensive deep-learning-based compression techniques can offer better compression ratios but are sometimes too slow for real-time applications;
• Interoperability: global adoption requires standards or negotiation mechanisms between heterogeneous devices;
• Security and privacy: compression before encryption, or its use alongside encryption, requires architectures that compromise neither security nor regulatory compliance;
• Integration costs: replacing or upgrading network equipment to support new methods can be expensive for operators.

Practical Recommendations

To respond effectively to the growing need for compression in network traffic, public and private stakeholders should:

• Invest in research into adaptive codecs and the integration of compression and encryption;
• Encourage standardization, including protocols for signaling compression capabilities and extensions for QUIC/HTTP3;
• Deploy compression solutions as close to the source as possible, at the edge and on IoT devices, to reduce traffic from the moment it is sent;
• Support cross-sector trials measuring actual gains in bandwidth, latency and energy consumption;
• Prepare to scale up decompression using hardware acceleration (ASICs, FPGAs and NPUs) to maintain low latency.

Conclusion

Network traffic compression is no longer merely a technical convenience: it is becoming an operational and environmental necessity. Physical network capacity is increasing, but not as fast as demand for functionality and quality. Advances—particularly evident in China, where investment is substantial—point the way forward: combining infrastructure, standards and advanced algorithms to create more efficient networks. Developing fast, adaptive, secure and easy-to-deploy compression methods is therefore imperative to ensure high-performing, resilient and sustainable digital services worldwide.

Sources

For further reading:

• Public analyses by operators and standards organizations on network capacity and codecs (IETF, ITU)
• Announcements and reports from Chinese technology companies and operators on 5G and CDN deployment and compression R&D
• Recent academic studies on the use of AI for compression and network optimization