Network planning is crucial to facilitate network service under limited network operation costs. However, adapting the network topology (i.e., connections and capacities for physical and IP links) to time-varying and stochastic network states is challenging due to high computational overhead of problem solving. Existing deep learning based methods suffer from prohibitive computational complexity due to simultaneously solving IP link capacities and allocated traffics or iteratively exploring search space with excessive samples via heuristic rules. In this paper, we propose GDPlan, the first generative framework that leverages conditional graph diffusion model to address this challenge. To achieve high solving efficiency, GDPlan decouples network planning into two separate stages, i.e., generation of diverse high-quality solutions to IP link capacity and refinement of the solutions under feasibility constraints. Specifically, we develop generation-oriented graph signal modeling that reformulates solving IP link capacities as generation of graph topology conditioned on the graph signals related to traffic demands, physical links and network costs. Consequently, energy-guided controllable graph generation is achieved to capture significant structural patterns of graph topology with score-based graph diffusion model and produce diverse solutions with a guarantee of feasibility under specific objectives or constraints. GDPlan successfully achieves graph generation based solution to network planning with an order of magnitude higher solving speed than the commercial solver Gurobi. Experimental results demonstrate that, compared with Gurobi, the proposed GDPlan obtains lowest 3.7% average gap with only about 7.5% average running time.

Image compression for machine vision exhibits various rate-accuracy performance across different downstream tasks and content types. An efficient utilization of constrained network resource for achieving an optimal overall task performance has thus recently attracted a growing attention. In this paper, we propose Tombo, a task-oriented image compression and transmission framework that efficiently identifies the optimal encoding bitrate and routing scheme for multiple image bitstreams delivered simultaneously for different downstream tasks. Specifically, we study the characteristics of image rate-accuracy performance for different machine vision tasks, and formulate the task-oriented joint bitrate and routing optimization problem for multi-bitstreams as a multi-commodity network flow problem with the time-expanded network modeling. To ensure consistency between the encoding bitrate and routing optimization, we also propose an augmented network that incorporates the encoding bitrate variables into the routing variables. To improve computational efficiency, we further convert the original optimization problem to a multi-marginal optimal transport problem, and adopt a Sinkhorn iteration-based algorithm to quickly obtain the near-optimal solution. Finally, we adapt Tombo to efficiently deal with the dynamic network scenario where link capacities may fluctuate over time. Empirical evaluations on three typical machine vision tasks and four real-world network topologies demonstrate that Tombo achieves a comparable performance to the optimal one solved by the off-the-shelf solver Gurobi, with a 5x ~ 114× speedup.

Viewers of 360-degree videos are provided with both visual modality to characterize their surrounding views and audio modality to indicate the sound direction. Though both modalities are important for saliency prediction, little work has been done by jointly exploiting them, which is mainly due to the lack of audio-visual saliency datasets and insufficient exploitation of the multi-modality. In this article, we first construct an audio-visual saliency dataset with 57 360-degree videos watched by 63 viewers. Through a deep analysis of the constructed dataset, we find that the human gaze can be attracted by the auditory cues, resulting in a more concentrated saliency map if the sound source's location is further provided. To jointly exploit the visual and audio features and their correlation, we further design a saliency prediction network for 360-degree videos (SVGC-AVA) based on spherical vector-based graph convolution and audio-visual attention. The proposed spherical vector-based graph convolution can process visual and audio features directly in the sphere domain, thus avoiding projection distortion incurred by traditional CNN-based predictors. In addition, the audio-visual attention scheme explores self-modal and cross-modal correlation for both modalities, which are further hierarchically processed with the U-Net's multi-scale structure of SVGC-AVA. Evaluations on both our and public datasets validate that SVGC-AVA can achieve higher prediction accuracy, both qualitatively and subjectively.