Abstract We present VLL-Codec, a Very Low Latency differential video codec designed for real-time streaming applications on CPU-only environments. The codec employs a lightweight frame-to-frame differential encoding strategy, leveraging block-level XOR delta computation combined with entropy-aware packing to minimize computational overhead while preserving temporal coherence. Unlike conventional video codecs that rely on motion vectors, transform-domain compression, and complex rate-control pipelines, VLL-Codec focuses on ultra-low processing latency and deterministic execution. The architecture eliminates floating-point transforms and heavy macroblock search procedures, enabling consistent sub-frame encoding times on commodity hardware. The codec is optimized for edge computing scenarios, embedded systems, mobile ARM platforms, and latency-critical streaming pipelines where deterministic performance is preferred over maximum compression ratio. Experimental validation demonstrates stable real-time operation under constrained hardware conditions without GPU acceleration or cloud offloading. This publication establishes prior art for the described differential codec architecture and its real-time CPU-first design philosophy. VLL-Codec: A Very Low Latency Differential Video Codec for Real-Time Streaming Applications DOI: 10.5281/zenodo.18773118 STATEMENT OF PRIOR ART AND LICENSE TERMS (PolyForm Noncommercial 1.0.0) 1. Academic and Research Use (Permitted) Free use is granted for: • Academic research• Independent study• Educational purposes• Benchmarking and comparative analysis• Testing within private or institutional systems• Reproducibility studies• Publication of analytical results Non-commercial experimentation and validation are expressly encouraged. 2. Statement of Prior Art This document constitutes a public disclosure establishing prior art for the VLL-Codec architecture and its differential encoding methodology. The disclosure is intended to prevent third-party patent claims on the conceptual framework described herein. 3. License Scope Conceptual methods are publicly disclosed. Source code, implementations, binaries, optimized builds, hardware realizations, and derivative systems remain protected intellectual property and are licensed under the PolyForm Noncommercial License 1.0.0. 4. Noncommercial Restriction Use is permitted exclusively for non-commercial purposes. Any integration into revenue-generating systems, proprietary software, streaming platforms, SaaS products, hardware devices, or commercial infrastructures constitutes commercial use. 5. Commercial Use Requirement Commercial deployment requires prior written authorization from the author. Any entity seeking to integrate VLL-Codec or derivative implementations into commercial systems must obtain explicit permission before use. 6. Anti-Circumvention Reimplementation, structural replication, architectural mimicry, translation to other programming languages, hardware description (HDL), or partial functional reproduction shall be considered derivative use. Refactoring, renaming, modular fragmentation, or algorithmic equivalence does not exempt compliance with the license. 7. Presumption of Derivation Systems exhibiting substantial structural or performance similarity developed after exposure to this work may be presumed derivative unless independently proven otherwise. 8. Knowledge Contamination Exposure to source code, documentation, or architectural description constitutes knowledge contamination. Subsequent similar implementations by exposed parties are not considered clean-room unless demonstrably developed prior to exposure. 9. Intellectual Property Ownership All intellectual property rights associated with the architecture, encoding methodology, optimization strategies, and structural layout remain exclusively owned by the author. No ownership rights are transferred under this license. 10. Remedies and Enforcement Violation of these terms may result in termination of license rights and pursuit of legal remedies as permitted by applicable law.
Andrés Sebastián Pirolo (Wed,) studied this question.