发表机构
Ericsson Research; AT&T（爱立信研究; 美国电话电报公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文设计了一种仅利用实验室现有能力的QoE感知速率自适应原型，通过复合空间复杂度和增量重分配算法，在不修改商业应用的情况下实现资源分配，并规划了向完全QoE感知共享的演进路径。
AI 中文摘要
先前的研究表明，与速率公平分配相比，面向实时交互视频的QoE感知资源共享在可接受质量下可支持多达三倍的同时会话数。然而，所需的能力（面向QoE目标的编码、运行时空间复杂度估计以及丰富的应用-网络API）在商业部署中尚不可用。在本文中，我们采取一种演进式方法：设计一个仅利用当前可在实验室中组装的能力即可实现QoE感知资源分配的系统。我们通过引入复合空间复杂度将基于效用的分配框架扩展到无线资源域，该复杂度将会话的视频空间复杂度与其时变频谱效率结合为单一资源需求函数。为了与采用基于速率的拥塞控制且缺乏QoE测量能力的商业实时视频流应用协同工作，我们使用外部工具进行QoE测量。我们开发了一种增量式重分配算法，该算法带有每间隔限制，这些限制既编码了拥塞控制算法的速度约束，也编码了空间复杂度估计仅在接近当前速率时才可靠的事实。由此产生的原型将外部QoE测量与基于拥塞信号的速率引导相结合，无需修改商业应用。我们绘制了从该原型到完全QoE感知资源共享的演进路径，将新兴标准（IETF SCONE、CAMARA、Media over QUIC）映射到它们所启用的渐进能力上。
英文摘要
Prior work has shown that QoE-aware resource sharing for real-time interactive video can support up to three times more simultaneous sessions at acceptable quality compared to rate-fair allocation. However, the required capabilities (QoE-targeted encoding, runtime spatial complexity estimation, and rich application-network APIs) are not yet available in commercial deployments. In this paper, we take an evolutionary approach: we design a system that delivers QoE-aware resource allocation using only capabilities that can be assembled in a lab today. We extend the utility-based allocation framework to the radio resource domain by introducing composite spatial complexity, which combines a session's video spatial complexity with its time-variant spectral efficiency into a single resource demand function. To operate with commercial real-time video streaming applications that use rate-based congestion control and lack capability to measure QoE, we use external tooling for QoE measurements. We develop an incremental reallocation algorithm with per-interval limits that encode both the congestion control algorithm's speed constraint and that spatial complexity estimates are reliable only near the current rate. The resulting prototype combines external QoE measurements with congestion-signal-based rate steering and does not require modification to commercial applications. We chart an evolution path from this prototype toward full QoE-aware resource sharing, mapping emerging standards (IETF SCONE, CAMARA, Media over QUIC) to the progressive capabilities they enable.
CommentsAccepted author manuscript. Published in Proc. IEEE QoMEX 2026, Cardiff, UK. (c) 2026 IEEE. 7 pages, 2 figures, 3 algorithms, 2 tables
Journal refProc. 2026 18th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2026, pp. 1-7
DOI：10.1109/QoMEX69967.2026.11618317
