MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning

The popularity of immersive videos has prompted extensive research into neural adaptive tile-based streaming to optimize video transmission over networks with limited bandwidth. However, the diversity of users' viewing patterns and Quality of Experience (QoE) preferences has not been fully addressed yet by existing neural adaptive approaches for viewport prediction and bitrate selection. Their performance can significantly deteriorate when users' actual viewing patterns and QoE preferences differ considerably from those observed during the training phase, resulting in poor generalization. In this paper, we propose MANSY, a novel streaming system that embraces user diversity to improve generalization. Specifically, to accommodate users' diverse viewing patterns, we design a Transformer-based viewport prediction model with an efficient multi-viewport trajectory input output architecture based on implicit ensemble learning. Besides, we for the first time combine the advanced representation learning and deep reinforcement learning to train the bitrate selection model to maximize diverse QoE objectives, enabling the model to generalize across users with diverse preferences. Extensive experiments demonstrate that MANSY outperforms state-of-the-art approaches in viewport prediction accuracy and QoE improvement on both trained and unseen viewing patterns and QoE preferences, achieving better generalization.Comment: This work has been submitted to the IEEE Transactions on Mobile Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field

From Capture to Display: A Survey on Volumetric Video

Volumetric video, which offers immersive viewing experiences, is gaining increasing prominence. With its six degrees of freedom, it provides viewers with greater immersion and interactivity compared to traditional videos. Despite their potential, volumetric video services poses significant challenges. This survey conducts a comprehensive review of the existing literature on volumetric video. We firstly provide a general framework of volumetric video services, followed by a discussion on prerequisites for volumetric video, encompassing representations, open datasets, and quality assessment metrics. Then we delve into the current methodologies for each stage of the volumetric video service pipeline, detailing capturing, compression, transmission, rendering, and display techniques. Lastly, we explore various applications enabled by this pioneering technology and we present an array of research challenges and opportunities in the domain of volumetric video services. This survey aspires to provide a holistic understanding of this burgeoning field and shed light on potential future research trajectories, aiming to bring the vision of volumetric video to fruition.Comment: Submitte

Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses the data sharing problem, enabling privacy-preserved collaborative training to provide sufficient precious data. Great potential, therefore, arises with the confluence of them, known as multimodal federated learning. However, limitation lies in the predominant approaches as they often assume that each local dataset records samples from all modalities. In this paper, we aim to bridge this gap by proposing an Unimodal Training - Multimodal Prediction (UTMP) framework under the context of multimodal federated learning. We design HA-Fedformer, a novel transformer-based model that empowers unimodal training with only a unimodal dataset at the client and multimodal testing by aggregating multiple clients' knowledge for better accuracy. The key advantages are twofold. Firstly, to alleviate the impact of data non-IID, we develop an uncertainty-aware aggregation method for the local encoders with layer-wise Markov Chain Monte Carlo sampling. Secondly, to overcome the challenge of unaligned language sequence, we implement a cross-modal decoder aggregation to capture the hidden signal correlation between decoders trained by data from different modalities. Our experiments on popular sentiment analysis benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that HA-Fedformer significantly outperforms state-of-the-art multimodal models under the UTMP federated learning frameworks, with 15%-20% improvement on most attributes.Comment: 10 pages,5 figure

ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration

The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference. Common video encoding configurations (e.g., resolution and frame rate) have been identified with significant impacts on striking the balance between bandwidth consumption and inference accuracy and therefore their adaption scheme has been a focus of optimization. However, previous profiling-based solutions suffer from high profiling cost, while existing deep reinforcement learning (DRL) based solutions may achieve poor performance due to the usage of fixed reward function for training the agent, which fails to craft the application goals in various scenarios. In this paper, we propose ILCAS, the first imitation learning (IL) based configuration-adaptive VA streaming system. Unlike DRL-based solutions, ILCAS trains the agent with demonstrations collected from the expert which is designed as an offline optimal policy that solves the configuration adaption problem through dynamic programming. To tackle the challenge of video content dynamics, ILCAS derives motion feature maps based on motion vectors which allow ILCAS to visually ``perceive'' video content changes. Moreover, ILCAS incorporates a cross-camera collaboration scheme to exploit the spatio-temporal correlations of cameras for more proper configuration selection. Extensive experiments confirm the superiority of ILCAS compared with state-of-the-art solutions, with 2-20.9% improvement of mean accuracy and 19.9-85.3% reduction of chunk upload lag.Comment: This work has been submitted to the IEEE Transactions on Mobile Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction

Volumetric video emerges as a new attractive video paradigm in recent years since it provides an immersive and interactive 3D viewing experience with six degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric videos require dense point clouds, voxels, meshes, or huge neural models to depict volumetric scenes, which results in a prohibitively high bandwidth burden for video delivery. Users' behavior analysis, especially the viewport and gaze analysis, then plays a significant role in prioritizing the content streaming within users' viewport and degrading the remaining content to maximize user QoE with limited bandwidth. Although understanding user behavior is crucial, to the best of our best knowledge, there are no available 3D volumetric video viewing datasets containing fine-grained user interactivity features, not to mention further analysis and behavior prediction. In this paper, we for the first time release a volumetric video viewing behavior dataset, with a large scale, multiple dimensions, and diverse conditions. We conduct an in-depth analysis to understand user behaviors when viewing volumetric videos. Interesting findings on user viewport, gaze, and motion preference related to different videos and users are revealed. We finally design a transformer-based viewport prediction model that fuses the features of both gaze and motion, which is able to achieve high accuracy at various conditions. Our prediction model is expected to further benefit volumetric video streaming optimization. Our dataset, along with the corresponding visualization tools is accessible at https://cuhksz-inml.github.io/user-behavior-in-vv-watching/Comment: Accepted by ACM MM'2

LiveVV: Human-Centered Live Volumetric Video Streaming System

Volumetric video has emerged as a prominent medium within the realm of eXtended Reality (XR) with the advancements in computer graphics and depth capture hardware. Users can fully immersive themselves in volumetric video with the ability to switch their viewport in six degree-of-freedom (DOF), including three rotational dimensions (yaw, pitch, roll) and three translational dimensions (X, Y, Z). Different from traditional 2D videos that are composed of pixel matrices, volumetric videos employ point clouds, meshes, or voxels to represent a volumetric scene, resulting in significantly larger data sizes. While previous works have successfully achieved volumetric video streaming in video-on-demand scenarios, the live streaming of volumetric video remains an unresolved challenge due to the limited network bandwidth and stringent latency constraints. In this paper, we for the first time propose a holistic live volumetric video streaming system, LiveVV, which achieves multi-view capture, scene segmentation \& reuse, adaptive transmission, and rendering. LiveVV contains multiple lightweight volumetric video capture modules that are capable of being deployed without prior preparation. To reduce bandwidth consumption, LiveVV processes static and dynamic volumetric content separately by reusing static data with low disparity and decimating data with low visual saliency. Besides, to deal with network fluctuation, LiveVV integrates a volumetric video adaptive bitrate streaming algorithm (VABR) to enable fluent playback with the maximum quality of experience. Extensive real-world experiment shows that LiveVV can achieve live volumetric video streaming at a frame rate of 24 fps with a latency of less than 350ms