arXiv:2608.10020eess.IVcs.MM2026-08中稿 · the 34th ACM Inter…

通过中继协调的组播技术,实现百人同时观看立体内容时低延迟、高效率传输。

MD2G-Cast: Relay-Coordinated Multicast for Scalable Volumetric Streaming over MoQ

论文配图:MD2G-Cast: Relay-Coordinated Multicast for Scalable Volumetric Streaming over MoQ
图 1 · 摘自论文原文
  • 基于视场重叠和网络条件动态组建可复用组播组
  • 100用户下99%延迟低于40毫秒,比滚动传输快十倍以上
  • 适合大规模沉浸式视频流场景,尤其对带宽敏感的应用

立体内容流媒体难以扩展,因视角重叠的接收端常被独立服务,导致共享内容重复传输。本文提出基于媒体过QUIC(MoQ)的中继协调组播框架MD2G-Cast,引入应用感知控制层实现多用户高效传输。该框架结合观看重叠、终端能力与带宽状况,动态形成可复用组播组,共享基础内容,并选择性提供增强传输。将分组与增强准入建模为序列控制问题,采用近端策略优化(PPO)实现,并通过教师指导训练轻量级中继模型。在真实接入与6自由度观看轨迹上,对最多100名用户的场景进行评估。在20和100用户下,接收端99%延迟均低于40毫秒,而滚动方案在多数情况下达到500毫秒上限。在所有用户规模下,MD2G-Cast在同质接入中取得最高或并列最高系统效用,在异质接入中表现最优,且相比聚类方法在100用户时降低约27%总链路负载。消融实验表明,中继控制结构本身优于随机可行动作,其性能不依赖特定优化器。结果表明,中继协调与选择性增强传输是核心贡献。

原文摘要 · Abstract (English)

Volumetric streaming remains difficult to scale because receivers with overlapping fields of view are often served independently, causing repeated transmission of shared content. We present MD2G-Cast, a relay-coordinated multicast framework over Media over QUIC with an application-aware control layer for scalable multi-user volumetric delivery. MD2G-Cast jointly uses viewing overlap, receiver capability, and bandwidth conditions to form reusable multicast groups, share common Base content, and selectively admit Enhanced delivery. We formulate grouping and Enhanced admission as a sequential control problem, realize it with Proximal Policy Optimization (PPO), and train a compact relay model with teacher guidance for Enhanced admission. We implement MD2G-Cast with real MoQ processes and evaluate it with real access and 6DoF viewing traces for up to 100 users. At 20 and 100 users, MD2G-Cast keeps the receiver-side $P_{99}$ delivery interval below 40 ms across all seven access profiles, while Rolling reaches the 500 ms reporting cap in most cases. Across the evaluated user scales, MD2G-Cast achieves the highest or tied-highest mean system utility under homogeneous access and the highest mean utility under heterogeneous access, while reducing aggregate link load by about 27% relative to Clustering at 100 users. A matched relay-control ablation separates the control structure from its optimizer, showing that random feasible actions reduce utility while deterministic control remains competitive with PPO. Together, the results support relay coordination and selective Enhanced admission, rather than a particular policy optimizer, as the central design contribution.

立体流媒体组播技术低延迟中继协调

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