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While theoretical QoE-aware cellular scheduling can triple interactive video capacity over rate-fair baselines, commercial deployments lack the necessary encoder hooks and in-app telemetry to support it. To resolve this, the authors construct an infrastructure-side prototype that estimates session demand by fusing time-variant radio spectral efficiency with video spatial complexity into a unified composite metric. By driving an incremental reallocation algorithm through non-invasive congestion signaling and external QoE tooling, the system achieves QoE-aware rate steering over unmodified, black-box commercial applications.
Real-time video capacity can triple over cellular networks today without modifying proprietary client applications by pairing composite spatial-spectral complexity modeling with non-invasive congestion-signal rate steering.
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.