TR 26.870 V0.2.12 – Study on Media Aspects for 6G System (Release 20)

Overview This is an early-stage Technical Report (SA4 study item FS_6G_MED) identifying media-related opportunities, gaps, and requirements for 6G. It is not yet approved and has no normative status. Conclusions will feed future detailed studies and normative work.

Scope and Alignment - Aligned with SA1 service requirements (TR 22.870), SA2 architecture study (TR 23.801-01), and existing SA4 5G media specs (TS 26.501, TS 26.506, TS 26.511, TS 26.512, TS 26.114, etc.) - Existing 5G media delivery architecture (streaming and real-time communication) is the baseline starting point

Five Work Topics Defined (all still under development) 1. WT#1 – Media Delivery Architecture: Harmonized 6G media delivery architecture building on TS 26.501/26.506; key issues include whether streaming and real-time communication architectures should be harmonized or separated, and which content delivery protocols to adopt/extend for 6G 2. WT#2 – 6G Media: Immersive/AI media trends, traffic characterization, QoE metrics, multimodal formats, AI-related media services (agents, LLMs, diffusion models), and 2D video codec updates (H.264/HEVC successors) 3. WT#3 – Media Aspects Related to SA2 Topics: Placeholder; content TBD 4. WT#4 – Media for Ubiquitous Access: Media over NTN and low-bitrate/low-power scenarios; references ULBC study (TR 26.940); key issues include NTN bitrate/latency/loss ranges and performance of existing services under such conditions 5. WT#5 – Trusted and Private Communication for Media: Placeholder; content TBD

Key Technical Observations (Annex A, partially agreed) - AI-enabled applications generate heterogeneous, multimodal, uplink-heavy, bursty traffic with diverse latency sensitivity - Current QoS frameworks may lack granularity and context-awareness for AI-driven traffic - Multi-device AI service scenarios are not well addressed by existing UE-centric assumptions - Embodied AI (robots, UAVs) may require 20–100 Mbit/s uplink with low latency and error resilience - Temporal synchronization across modalities (video, audio, haptics, AI data) is a key challenge

Tooling Contributions (Annexes B & C) - Network Emulator (Annex B): Linux tc/netem-based emulator with YAML-configured profiles; includes 5QI-derived profiles (5QI 1/2/7/80), scenario profiles (ideal 6G, 5G urban, satellite, cell edge, congested), and advanced impairment models (Gilbert-Elliott loss, HTB shaping). Hosted at GitHub (5G-MAG/6G-Testbed) - AI Traffic Characterization Testbed (Annex C): End-to-end orchestration framework for AI service traffic measurement; supports OpenAI/Gemini/DeepSeek/vLLM providers; captures L3/L4 (pcap) and L7 (mitmproxy); logs TTFT, TTLT, token rate, burstiness, UL/DL bytes to SQLite. Use of TTFT/TTLT for AI traffic evaluation is FFS.

Status Document is v0.2.12; most clause content remains under editor's notes. Findings (clause 7) and recommendations (clause 8) are empty pending completion of work topics.

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

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

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

pCR Summary: TR 26.870 – Key Issue 1 Update on Media Delivery Architecture Harmonization

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

What is being proposed: - A partial CR (pCR) to update Section 6.2.1.5 of TR 26.870 (FS_6G_MED study item), adding discussion directions under Key Issue 1.

Changes made: - Two study questions are added to Section 6.2.1.5: 1. Whether the media delivery architecture for streaming and real-time communication services should be harmonized or separated, and what the trade-offs are between shared components vs. service-specific optimization. 2. Which existing and emerging content delivery protocols map to the 5G Media Delivery architecture, and what extensions/simplifications are applicable for 6G Media Delivery.

Context: - The starting point for discussion is the existing 5G Media Delivery architecture. - The study aims to address a diverse set of media applications and traffic patterns under the 6G media delivery framework.

Nature of change: Editorial/structural — adds scoping questions to guide further study; no normative technical decisions are made.

Action requested: Approval.