Coordinated Networking for On-Device Agent-Augmented Real-Time Communication
HAFS framework improves video quality by 1.5x and reduces response time by 31%.
Key Findings
Methodology
The HAFS framework uses an app-guided multi-flow transport approach, coordinating the sending rates of video and agent context flows at the application layer. A prototype built on WebRTC and llama.cpp demonstrates its effectiveness.
Key Results
- HAFS improved video quality by 1.5x while reducing agent response time by 31%.
- HAFS maintained efficient performance across various devices and network environments.
- Compared to existing baselines, HAFS showed significant improvements in both video quality and response time.
Significance
This research provides a new solution for on-device agent-augmented real-time communication, addressing privacy and cost issues of cloud-based agents. It optimizes network traffic to enhance video quality and agent response speed.
Technical Contribution
The HAFS framework achieves precise control over multi-flow transport at the application layer, overcoming traditional methods' feedback delay issues on long RTT links, offering new engineering possibilities.
Novelty
This is the first implementation of agent-augmented real-time communication on-device, with innovative multi-flow coordination at the application layer.
Limitations
- HAFS performance may be affected in high-load network environments.
- Optimization for different devices is needed to achieve best performance.
Future Work
Future research could explore HAFS applicability in more scenarios and optimize its performance under varying network conditions.
AI Executive Summary
With the rise of AI agents, real-time communication applications are evolving towards agent-augmented models. However, existing cloud-based agents face privacy and cost challenges, making on-device agents a promising alternative. The HAFS framework addresses network contention issues in on-device agent-augmented real-time communication through an app-guided multi-flow transport method.
The HAFS framework coordinates the sending rates of video and agent context flows at the application layer, ensuring high video quality and low agent response time. Experimental results show that HAFS improved video quality by 1.5x while reducing agent response time by 31%.
This research provides a new solution for on-device agent-augmented real-time communication, addressing privacy and cost issues of cloud-based agents. Future research could explore HAFS applicability in more scenarios and optimize its performance under varying network conditions.
Deep Analysis
Background
With advancements in AI, real-time communication applications are increasingly moving towards agent-augmented models. While powerful, existing cloud-based agents face privacy and cost issues. On-device agent-augmented real-time communication offers a new solution, allowing agents to run locally on user devices, reducing cloud GPU costs and keeping user data encrypted during transmission.
Core Problem
The main issue in on-device agent-augmented real-time communication is network traffic contention. Human video streams and agent context flows compete for shared network bandwidth, leading to degraded video quality and increased agent response time.
Innovation
The HAFS framework employs an app-guided multi-flow transport approach, coordinating the sending rates of video and agent context flows at the application layer. This method uses video frame-level queuing delay as a guiding signal to ensure high video quality and low agent response time.
Methodology
- �� Implement unified coordination of multi-flow transport at the application layer.
- �� Use video frame-level queuing delay as a guiding signal.
- �� Prototype built on WebRTC and llama.cpp for validation.
Experiments
Experiments were conducted across various devices and network environments, including MacBook Pro, Samsung Galaxy S25, and NVIDIA Jetson. Using Wi-Fi 6 and 5G networks, HAFS performance in video quality and agent response time was evaluated.
Results
HAFS improved video quality by 1.5x while reducing agent response time by 31%. HAFS maintained efficient performance across various devices and network environments.
Applications
HAFS can be applied in scenarios such as corporate document collaboration, remote healthcare, and media co-creation, providing efficient real-time communication solutions.
Limitations & Outlook
HAFS performance may be affected in high-load network environments. Optimization for different devices is needed to achieve best performance.
Plain Language Accessible to non-experts
Imagine a kitchen where chefs are preparing a large meal. Each chef has their task, but they need to coordinate the use of kitchen equipment. HAFS acts like a smart kitchen manager, ensuring each chef can efficiently use the equipment without interfering with one another. This way, the kitchen can quickly prepare delicious dishes.
ELI14 Explained like you're 14
Imagine you're playing a multiplayer online game with friends. Everyone has their tasks, but you need to coordinate to win the game. HAFS is like a smart team captain, ensuring everyone can efficiently complete their tasks without interfering with each other. This way, you can win the game faster!
Glossary
HAFS (Human-Agent Flow Scheduling)
A network coordination framework for on-device agent-augmented real-time communication.
Used to coordinate the sending rates of video and agent context flows.
WebRTC
An open-source project that enables real-time communication, allowing peer-to-peer audio and video data transmission.
One of the infrastructures for the HAFS framework.
llama.cpp
An open-source library for building AI agents, supporting multi-modal input processing.
A component in HAFS for agent context flow processing.
KV Cache
A caching mechanism for storing context data, optimizing agent response time.
Used in agent context flows to reduce LLM prefill time.
Delay-based Congestion Control
A network traffic control mechanism aimed at reducing transmission delay.
Used for video stream transmission control.
Open Questions Unanswered questions from this research
- 1 How to optimize HAFS performance under extreme network conditions?
- 2 What is HAFS applicability in different application scenarios?
- 3 How to further reduce agent response time?
Applications
Immediate Applications
Corporate Document Collaboration
Using the HAFS framework, improve remote collaboration efficiency, ensuring high-quality video conferencing experience.
Long-term Vision
Remote Healthcare
Provide privacy-protected remote healthcare services through on-device agents, reducing cloud costs.
Abstract
AI agents are enabling a new paradigm of agent-augmented real-time communication (RTC), where humans focus on high-level collaboration, while agents autonomously retrieve, analyze, and generate information in real time to support their interactions. These apps enable new experiences across various domains: for example, when corporate employees co-author a legal document, their agents can discuss and draft on their behalf, sparing them the burden of manually reviewing each other's work. As existing cloud-based agents suffer from privacy risks and unscalable server costs, on-device agent-augmented RTC offers a promising alternative. However, this on-device paradigm introduces a new networking challenge: contention between concurrent traffic flows generated by humans (for live video streaming) and agents (for sending context files for analysis). We design HFS, a framework to ensure both high live video quality and low agent response latency in agent-augmented RTC apps. We achieve the goal through an app-guided multi-flow transport approach, where a unified app-layer orchestrator jointly controls the sending rates of live video and agent context flows based on their heterogeneous app requirements. Our prototype built atop WebRTC and llama.cpp demonstrates that HAFS outperforms baselines, achieving 1.5x higher video quality while reducing agent response time by 31%.