Delay-Constrained Video Transmission: Quality-driven Resource Allocation and Scheduling

Real-time video demands quality-of-service (QoS) guarantees such as delay bounds for end-user satisfaction. Furthermore, the tolerable delay varies depending on the use case such as live streaming or two-way video conferencing. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are difficult to guarantee. Instead, we propose providing statistical delay guarantees using the concept of effective capacity. We consider a multiuser setup whereby different users have (possibly different) delay QoS constraints. We derive the resource allocation policy that maximizes the sum video quality and applies to any quality metric with concave rate-quality mapping. We show that the optimal operating point per user is such that the rate-distortion slope is the inverse of the supported video source rate per unit bandwidth, a key metric we refer to as the source spectral efficiency. We also solve the alternative problem of fairness-based resource allocation whereby the objective is to maximize the minimum video quality across users. Finally, we derive user admission and scheduling policies that enable selecting a maximal user subset such that all selected users can meet their statistical delay requirement. Results show that video users with differentiated QoS requirements can achieve similar video quality with vastly different resource requirements. Thus, QoS-aware scheduling and resource allocation enable supporting significantly more users under the same resource constraints.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin

Loss Visibility Optimized Real-time Video Transmission over MIMO Systems

The structured nature of video data motivates introducing video-aware decisions that make use of this structure for improved video transmission over wireless networks. In this paper, we introduce an architecture for real-time video transmission over multiple-input multiple-output (MIMO) wireless communication systems using loss visibility side information. We quantify the perceptual importance of a packet through the packet loss visibility and use the loss visibility distribution to provide a notion of relative packet importance. To jointly achieve video quality and low latency, we define the optimization objective function as the throughput weighted by the loss visibility of each packet, a proxy for the total perceptual value of successful packets per unit time. We solve the problem of mapping video packets to MIMO subchannels and adapting per-stream rates to maximize the proposed objective. We show that the solution enables jointly reaping gains in terms of improved video quality and lower latency. Optimized packet-stream mapping enables transmission of more relevant packets over more reliable streams while unequal modulation opportunistically increases the transmission rate on the stronger streams to enable low latency delivery of high priority packets. We extend the solution to capture codebook-based limited feedback and MIMO mode adaptation. Results show that the composite quality and throughput gains are significant under full channel state information as well as limited feedback. Tested on H.264-encoded video sequences, for a 4x4 MIMO with 3 spatial streams, the proposed architecture achieves 8 dB power reduction for the same video quality and supports 2.4x higher throughput due to unequal modulation. Furthermore, the gains are achieved at the expense of few bits of cross-layer overhead rather than a complex cross-layer design.Comment: Submitted to IEEE Transactions on Circuits and Systems for Video Technolog