## pCR Summary: S4-260561 – Network-Assisted Media Processing in TR 26.870

**Source:** Huawei | **Spec:** TR 26.870 | **Meeting:** SA4#135-bis-e

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### What is Being Proposed

This pCR proposes additions to TR 26.870 (FS_6G_MED) to introduce network-assisted media processing as a study topic within the 6G media delivery architecture, motivated by the computational gap between device capabilities and AI-based video processing demands.

### Technical Rationale

- Video LLMs (e.g., Qwen3-VL-8B, InternVL3.5) require **270–1950 TFLOPS per image** at 1080p; current mobile devices offer ~40 TFLOPS — making on-device video understanding infeasible
- Video pre-processing tasks (deblurring via DeblurGAN/MPRNet, super-resolution via ESRGAN/SwinIR, low-light enhancement via Zero-DCE/RetinexNet) require 0.5–3.5 TFLOPS/frame — also challenging for lightweight devices (AR glasses, etc.)
- Motivation drawn from SA1 TR 22.870 use cases: AI-based video analysis (6.19), mobile embodied AI offloading (6.28), AI for disability support (6.38), XR rendering offload (9.4)

### Proposed Changes to TR 26.870

1. **Clause 6.2.1.1** – Adds "network-assisted media processing for compute-intensive tasks" to the list of architectural aspects to be studied
2. **New Clause 6.2.1.X** – Introduces "Media Functional Entities for Network-Assisted Processing," covering: network-side video understanding (analysis, scene recognition, object detection), network-side video pre-processing (deblurring, super-resolution, low-light enhancement), and dynamic offloading decisions based on device/network conditions
3. **Clause 6.2.1.5 (Key Issues)** – Adds a new key issue: how Media Functional Entities can be enhanced to support network-assisted media processing for resource-constrained devices
4. **New Annex A.2.x** – Documents the SA1 use cases and observations/proposals as study background for multi-modal data processing

### Proposals/Conclusions

- **Observation 1:** Network side should undertake compute-intensive video understanding (videoLLMs) on behalf of devices
- **Observation 2:** Network side should handle video pre-processing for lightweight devices
- **Proposal 1:** Explore architectural impacts of introducing videoLLMs into the media delivery architecture
- **Proposal 2:** Explore enhancements to Media Functional Entities to support video pre-processing capabilities