MD2G-Cast：基于MoQ的可扩展体积流传输的中继协调组播
MD2G-Cast: Relay-Coordinated Multicast for Scalable Volumetric Streaming over MoQ
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中文总结 AI 辅助
该研究提出基于MoQ的MD2G-Cast中继协调组播框架，通过PPO算法实现分组与增强准入控制，可提升体积流传输的可扩展性，降低链路负载，优于Rolling和Clustering等方法。
中文摘要 AI 辅助
体积流传输的可扩展性仍存在困难，因为视场重叠的接收端通常被独立服务，导致共享内容的重复传输。我们提出MD2G-Cast，这是一个基于Media over QUIC（MoQ）的中继协调组播框架，配备了应用感知控制层，用于可扩展的多用户体积交付。MD2G-Cast联合利用视场重叠、接收端能力和带宽条件来形成可重复使用的组播组，共享通用基础（Base）内容，并选择性地允许增强（Enhanced）交付。我们将分组和增强准入问题建模为序列控制问题，通过近端策略优化（Proximal Policy Optimization，PPO）实现，并借助教师指导训练用于增强准入的紧凑中继模型。我们通过真实的MoQ流程实现MD2G-Cast，并使用真实的接入和6DoF视场轨迹对多达100个用户进行评估。在20和100个用户场景下，MD2G-Cast在全部7种接入配置文件下均将接收端侧的P99交付间隔保持在40毫秒以下，而Rolling方法在大多数情况下达到500毫秒的报告上限。在评估的用户规模范围内，MD2G-Cast在同质接入下实现最高或并列最高的平均系统效用，在异质接入下实现最高的平均效用，同时在100个用户时相较于Clustering方法降低约27%的聚合链路负载。匹配的中继控制 ablation 实验将控制结构与其优化器分离，结果显示随机可行动作会降低效用，而确定性控制与PPO相比仍具有竞争力。总体而言，这些结果表明中继协调和选择性增强准入是核心设计贡献，而非特定策略优化器。
英文摘要
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.
