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Viewport-dependent streaming (VDS) of immersive video typically devises the attentive viewport (or FoV - Field of View) with high-quality compression but low-quality compressed content outside of it to reduce bandwidth. It, however, assumes uniform compression within the viewport, completely neglecting visual redundancy caused by non-uniform perception in central and peripheral vision areas when consuming the content using a head-mounted display (HMD). Our work models the unequal retinal perception within the instantaneous viewport and explores using it in the VDS system for non-uniform viewport compression to further save the data volume. To this end, we assess the just-noticeable-distortion moment of the rendered viewport frame by carefully adapting image quality-related compression factors like quantization stepsize q and/or spatial resolution s zone-by-zone to explicitly derive the imperceptible quality perception threshold with respect to the eccentric angle. Independent validations show that the visual perception of the immersive images with non-uniform FoV quality guided by our model is indistinguishable from that of images with default uniform FoV quality. Our model can be flexibly integrated with the tiling strategy in popular video codecs to facilitate non-uniform viewport compression in practical VDS systems for significant bandwidth reduction (e.g., about 40% reported in our experiments) at similar visual quality. [ABSTRACT FROM AUTHOR]

Loki is a state-of-the-art adaptive bitrate algorithm for the transmission of real-time-communication (RTC) video. It fuses traditional heuristic methods with a learning-based model to maximize the quality of experience (QoE) under diverse network conditions. However, a recurring rebound pattern is observed in Loki’s decision-making process where the decision frequently oscillates between the two boundaries of the action space, making Loki fail to adapt to the fluctuating network bandwidth. To address this issue, we propose Loki+, which improves both the fusion mechanism and the design of the learning-based actor. Specifically, we replace the element-wise multiplication with a simple but effective trend fusion and further optimize the design of reward and loss functions for training Loki+. Extensive simulation results show that Loki+ significantly improves the QoE in the aspects of reducing the stall rate by 20% ∼ 60% and the frame delay by 3.5% ∼ 30.5% while maintaining a similar sending bitrate or video quality, compared with Loki. [ABSTRACT FROM AUTHOR]