Recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated its potential for efficient and photorealistic 3D reconstructions, which is crucial for diverse applications such as robotics and immersive media. However, current Gaussian-based methods for dynamic scene reconstruction struggle with large inter-frame displacements, leading to artifacts and temporal inconsistencies under fast object motions. To address this, we introduce TrackerSplat, a novel method that integrates advanced point tracking methods to enhance the robustness and scalability of 3DGS for dynamic scene reconstruction. TrackerSplat utilizes off-the-shelf point tracking models to extract pixel trajectories and triangulate per-view pixel trajectories onto 3D Gaussians to guide the relocation, rotation, and scaling of Gaussians before training. This strategy effectively handles large displacements between frames, dramatically reducing the fading and recoloring artifacts prevalent in prior methods. By accurately positioning Gaussians prior to gradient-based optimization, TrackerSplat overcomes the quality degradation associated with large frame gaps when processing multiple adjacent frames in parallel across multiple devices, thereby boosting reconstruction throughput while preserving rendering quality. Experiments on real-world datasets confirm the robustness of TrackerSplat in challenging scenarios with significant displacements, achieving superior throughput under parallel settings and maintaining visual quality compared to baselines. The code is available at https://github.com/yindaheng98/TrackerSplat.

Emerging live video streaming applications, e.g., Ultra High Definition videos and interactive video streaming, have put forward new demands for ultra-high bandwidth and reduced delay to match the desired quality of experience. Since current on-device Super-Resolution (SR) approaches are hindered by the limited end-device capabilities, we are motivated to take advantage of Mobile Edge Computing (MEC) and Computing in the Network technologies, such that SR videos can be processed on a more powerful infrastructure by integrating the resources from end-devices through the edge and all the way to the cloud. However, the integration of SR and MEC is non-trivial due to the challenges introduced by the features of SR tasks, and the heterogeneous nature of MEC resources. In this paper, we endeavor to explore and solve these challenges by presenting AVSA, which renders SR live video streaming efficiently with in-network computing. AVSA can adaptively allocate SR work-loads under heterogeneous resources and yield a cost-effective workload allocation with theoretical performance guarantees. Simulation results show that, compared with the state-of-the-art methods, our method achieves up to 13.06× speedup in terms of end-to-end latency.

Given the truly immersive viewing experiences, full-scene volumetric videos have received increasing attention from both academia and industry. Their vast data volumes, however, present significant challenges for real-time streaming over today's bandwidth-limited Internet. Considering the vast amount of full-scene volumetric data to be streamed and the limited bandwidth on the Internet, achieving adaptive full-scene volumetric video streaming over the Internet presents a significant challenge. Inspired by the advantages offered by neural fields, especially the feature grid method, we propose FSVFG, a novel full-scene volumetric video streaming system integrated feature grids as the representation of volumetric content. FSVFG employs an incremental training approach for feature grids and stores the features and residuals between adjacent grids as frames. To support adaptive streaming, we delve into the data structure and rendering processes of feature grids and propose bandwidth adaptation mechanisms. The mechanisms involve a coarse ray-marching for the selection of features and residuals to be sent, and achieve variable bitrate streaming by Level-of-Detail (LoD) and residual filtering. Based on these mechanisms, FSVFG achieves adaptive streaming by adaptively balancing the transmission of feature and residual according to the available bandwidth. Our preliminary results demonstrate the effectiveness of FSVFG, demonstrating its ability to improve visual quality and reduce bandwidth requirements of full-scene volumetric video streaming.

Video has become the primary source of Internet traffic due to the advance of streaming media technology and the surge in user demand for real-time video streaming applications. In this case, neural-enhanced video streaming and mobile edge computing are proposed to improve video content quality on nearby MEC (Mobile Edge Computing) devices and to maintain low interaction delay under limited bandwidth. In order to maximize the architectural advantages brought by MEC, we designed HyperRTV, a terminal-edge collaborative real-time video transmission system with neural-enhanced streaming. HyperRTV consists of three key components: 1) a video super-resolution structure that uses hardware-accelerated DNNs to satisfy latency limits; 2) an adaptive bitrate controller that can dynamically adjust the bitrate under various network conditions; 3) a heuristic task offloading strategy based on the ant colony algorithm to further meet the heterogeneity and dynamics of the MEC environment. Experiments show that HyperRTV decreases bandwidth consumption by 46% on average and achieves a 48%-65% reduction in latency, consistently maintaining better visual quality and higher stability in unpredictable networks. Our task offloading strategy can reduce the execution and transfer time by 16.1%-19.5% in a MEC environment with multi-task requests.

Live video streaming is increasingly popular for its rich content and real-time interactions, but its demand for bandwidth has put a heavy burden on backbone networks. To save bandwidth, recent studies have proposed neural-enhanced live video streaming that deploys deep neural networks (DNNs) for video super-resolution (VSR) on end devices or nearby edge devices to enhance video quality by taking low-resolution frames as input and producing high-resolution output frames. In this solution, the high computational demands of high-quality VSR DNNs make them difficult to support on single end or edge device, necessitating the use of distributed resources in edge facilities. However, the distributed deployment of high-quality VSR DNNs for low-latency inference remains challenging due to the inherent data dependencies of VSR DNNs and the heterogeneity and dynamics of edge facilities. In this paper, we present WAEVSR, a novel collaborative neural-enhanced live video super-resolution system that enables effective leverage of distributed resources to maximize the latency-bounded quality in wide-area MEC environments. WAEVSR consists of two key components: 1) It deploys a parallel-friendly video super-resolution DNN among edge devices, 2) with an inference controller based on the variable-size sliding window to balance the latency and quality of distributed inference in the heterogeneous and dynamics MEC environment. Prototype-based evaluation shows that WAEVSR can achieve 2.5 × lower end-to-end latency than traditional super-resolution serving with a 0.01 drop in SSIM score. The case study also demonstrates its higher stability on latency than vanilla distributed MEC deployment.

With Computing in the Network technologies, Mobile Edge Computing (MEC) has expanded the resource distribution and tightly integrated computing-network capabilities from the end-devices, through the edge, to the cloud infrastructure, including at points in between. Thus, edge computing is able to deliver a more collaborative processing, better service responding to the increasing application needs in low latency processing. In the presence of integrated computing-network resources and their increased capacity, current proximity-to-data methods in edge computing lead to sub-optimal performance in terms of processing latency. Addressing this issue, this paper presents a Low-latency Adaptive Workload Allocation framework (LAWA) to harness the growing in-network computing resources to deliver low latency processing capabilities for emerging latency-constrained applications. LAWA defines an application by its computational source and destination. Considering the diversity of computing and network resources, we try to find an optimal computational path and its workload allocation. We model the problem as a mixed integer programming problem. To solve this problem, we propose the computational pathfinding and workload allocation algorithms with optimality guarantees. Experimental results show that, comparing with the state-of-the-art methods, our method achieves up to 8.04× speedup, in terms of end-to-end latency.