Diffusion models generate images with an unprece-dented level of quality, but how can we freely rearrange image layouts? Recent works generate controllable scenes via learning spatially disentangled latent codes, but these methods do not apply to diffusion models due to their fixed forward process. In this work, we propose SceneDiffusion to optimize a layered scene representation during the diffusion sampling process. Our key insight is that spatial disentanglement can be obtained by jointly denoising scene rende rings at different spatial layouts. Our generated scenes support a wide range of spatial editing operations, including moving, resizing, cloning, and layer-wise appearance editing operations, including object restyling and replacing. Moreover, a scene can be generated conditioned on a ref-erence image, thus enabling object moving for in-the- wild images. Notably, this approach is training-free, compatible with general text-to-image diffusion models, and responsive in less than a second.

Real-time video transcoding that is often needed for robust video broadcasting over heterogeneous networks is not supported in most existing devices. To address this problem, we propose a collaborative strategy that leverages the peering architecture of peer-to-peer Internet protocol television networks and makes the computational resources of peers sharable. The video transcoding task is distributed among the peers and completed collaboratively. A prototype of the live video broadcasting system is evaluated over a 100-node testbed on the PlanetLab. The experimental results show that the proposed strategy works effectively even when the majority of the peers have limited computational resource and bandwidth.

HotStreaming is an overlay peer-to-peer (P2P) based IPTV system. It integrates the innovations in both overlay networking and video coding for optimal user experience. The HotStreaming system is composed of three key components: partnership formation, data request scheduling and multiple description coding (MDC). In the partnership formation component, we propose two policies to reduce the time of disconnection and the number of isolated peers. In the MDC component, we adopt MDC with spatial-temporal hybrid interpolation (MDC-STHI), which makes the peers in a P2P network to adjust the streaming traffic according to their bandwidth limitation and capability of devices. The experimental results show that the HotStreaming system improves the video quality over a lossy and dynamic networking environment.

NTUStreaming is an overlay P2P-based IPTV system that integrates innovations in both overlay networking and video coding for optimal user experience. The system consists of three key components: partnership formation, robust video coding, and video segment request scheduling. For partnership formation, a graph construction mechanism TYPHOON based on epidemic algorithms is developed to reduce disconnect time and isolated peers. For robust video coding, a multiple description coding (MDC) scheme with spatial-temporal hybrid interpolation (STHI) is proposed to adjust streaming traffic according to the bandwidth and device capability of each peer. For request scheduling, an optimization algorithm is developed by taking the available bandwidth and the video segment type into account. Experimental results show that NTUStreaming is able to deliver optimal video quality in lossy and dynamic networking environments.