Summary of S4-260112: AI/ML Media Processing and Task Updating

Document Overview

This contribution proposes updates to AI/ML media processing procedures and task updating call flows for IMS Data Channel (DC) applications. It builds upon TR 26.927 and TS 23.228 Annex AC, incorporating agreements from SA4#134 (S4-252075) and addressing feedback from SA2's reply LS on AIML for Media.

Main Technical Contributions

1. Refinement of AI/ML Task Processing Call Flows

Issues Identified with TR 26.927

• Architectural ambiguity: Split media processing location (UE vs network) was unclear
• DC AS introduction timing: Not properly specified after ADC establishment
• Confusing step numbering: Parallel options (5a, 5b) caused confusion
• MRF references: MRF should be removed as SA2 clarified it doesn't play a role in Data Channel (removed from TS 23.228)
• Incomplete task update procedures: Steps 9-10 lacked detail on how UE updates AI/ML inference tasks

Updated Call Flow Structure (Steps 1-23)

The revised flow incorporates common call flows agreed in S4-252075:

Initial Setup (Steps 1-13):
- UE1 registers to IMS with AI/ML capability indication
- MMTel session establishment between UE1 and UE2
- Bootstrap Data Channel (BbDC) establishment between UE1 and MF
- DCSF creates DC application list based on:
- Subscription list filter
- UE static capabilities
- Application list includes AI service information (e.g., intelligent translation service)
- UE1 downloads application list and selects application
- Application Data Channel (AaDC) establishment between UE1 and DC AS
- Task selection and AI/ML model selection

Media Processing Execution (Steps 14-16):
- Media session runs over MMTel session
- UE1 executes selected task and transmits input media streams
- Network runs inference and forwards processed streams to UE2 (or UE1, or both depending on application)
- Different alternatives supported based on inference location (local/remote/split)

2. Task Reselection and Update Mechanisms

Task Reselection (Step 17)

• Trigger: New actions in applications or other triggers during session
• Process: UE1 reselects tasks from previously downloaded task metadata
• Flow: Returns to step 10 (task selection from app manifest)

Task Update (Steps 17-23)

• Use Case: New requirements during running IMS session not fulfilled by downloaded tasks
• Example: New callee (UE3) joins call speaking new language requiring additional translation

Update Procedure:
- Step 17: UE1 sends UPDATE Task request over ADC with:
- Task ID
- New parameters
- Start time (when to apply new parameters)
- Optional additional parameters

• Steps 18-19:
• MF checks request and reconfigures task
• MF may reject invalid requests
• MF may establish new application DC or media flows if needed
• MF may stop existing flows no longer needed
• MF forwards UPDATE Task request to DC AS if needed
• DC AS reconfigures task according to new parameters

Alternative Execution Paths (Steps 20-22):
- Alt a - Local Inference:
- DC AS sends UPDATE Task response (including new models) to UE1 via MF
- UE1 runs updated inference task locally

• Alt b - Remote/Split Inference:
• DC AS sends UPDATE Task response to UE1 via MF
• UE1 transmits media streams to network for inference

• Network runs inference and forwards processed streams to UE2

• Step 23: Remote UE (UE2) informed when task updates impact it

3. Task Control Messages

3.1 START Task Message

Purpose: Request to start an inference task (for split or remote inference)

Message Content:
- id: Message identifier
- type: "urn:3gpp:aiml:start-task"
- task_id: Task identifier (e.g., "speech-to-speech-translation")
- parameters: Task-specific parameters (e.g., inputLanguage, outputLanguage)
- input: Protocol and media stream identifier (mid from SDP)
- output: Protocol and media stream identifier
- timestamp: Timestamp of request

Response Message:
- task_session_id: Unique identifier for specific task instance
- response_code: Status (e.g., "200 OK")
- Echoes task_id and parameters

Media Stream Identification: Uses "mid" identifier from RFC 8843 as included in SDP offer/answer. Multiple RTP streams identified by comma-separated mid values.

3.2 UPDATE Task Message

Purpose: Update existing task that has already been started (requires prior 200 OK response to START Task)

Use Cases:
- Update model, parameters, input or output of existing task
- Indicate new input/output stream (e.g., new UE added to call)

Message Content:
- id: Message identifier
- type: "urn:3gpp:aiml:update-task"
- task_id: Task identifier
- task_session_id: References task from START Task Response
- parameters: Updated parameters
- output: Updated output stream information
- timestamp: Timestamp of request

Response Message:
- task_session_id: Same as in request
- response_code: Status indication
- Confirms task_id

Key Technical Clarifications

Inference Location Flexibility

The specification supports three inference deployment models:
1. Local inference: AI model downloaded and executed in UE
2. Remote inference: Inference executed in network (MF)
3. Split inference: Inference split between UE and network

Message Exchange Protocol

• Task control messages exchanged over Application Data Channel (AaDC)
• Messages use structured format with JSON-like syntax
• Unique identifiers (task_session_id) maintain task context across updates

Network Entity Roles

• DCSF: Creates and filters DC application list based on subscription and UE capabilities
• MF: Manages media flows, coordinates with DC AS, executes inference tasks
• DC AS: Provides AI applications and models, reconfigures tasks
• MRF: Explicitly removed from procedures (per SA2 clarification)

Editorial Notes

• Network functional entity for inference task execution depends on SA2's reply LS
• Further details on message formats to be provided in future contributions