Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods

Robust and Efficient Activity Recognition from Videos

With technological advancement in embedded system design, powerful cameras have been embedded within smart phones, and wireless cameras can be easily deployed at street corners, traffic lights, big stadiums, train stations, etc. Besides, the growth of online media, surveillance, and mobile cameras have resulted in an explosion of videos being uploaded to social media sites such as Facebook and YouTube. The availability of such a vast volume of videos has attracted the computer vision community to conduct much research on human activity recognition since people are arguably the most interesting subjects of such videos. Automatic human activity recognition allows engineers and computer scientists to design smarter surveillance systems, semantically aware video indexes and also more natural human-computer interfaces. Despite the explosion of video data, the ability to automatically recognize and understand human activities is still rather limited. This is primarily due to multiple challenges inherent to the recognition task, namely large variability in human execution styles, the complexity of the visual stimuli in terms of camera motion, background clutter, viewpoint changes, etc., and the number of activities that can be recognized. In addition, the ability to predict future actions of objects based on past observed video frames is very useful. Therefore, in this thesis, we explore four designs to solve the problems we discussed earlier, namely (1) A semantics-based deep learning model, namely SBGAR, is proposed to do group activity recognition. This model achieves higher accuracy and efficiency than existing group activity recognition methods. (2) Despite its high accuracy, SBGAR has some limitations, namely (i) it requires a large dataset with caption information, (ii) activity recognition model is independent of the caption generation model and hence SBGAR may not perform well in some cases. To remove such limitations, we design ReHAR, a robust and efficient human activity recognition scheme. ReHAR can be used to recognize both single-person activities and group activities. (3) In many application scenarios, merely knowing what the moving agents are doing is not sufficient. It also requires predictions of future trajectories of moving agents. Thus, we propose GRIP, a graph-based interaction-aware motion intent prediction scheme. The scheme uses a graph to represent the relationships between two objects, e.g., human joints or traffic agents, and predict the motion intents of all observed objects simultaneously. (4) Action recognition and trajectory prediction schemes are typically deployed in resource-constrained devices. Thus, any technique that can accelerate the computation speed of our schemes is important. Hence, we propose a novel deep learning model decomposition method called DAC that is capable of factorizing an ordinary convolutional layer into two layers with much fewer parameters. DAC computes the corresponding weights for the newly generated layers directly from the weights of the original convolutional layer. Thus, no training (or fine-tuning) or any data is needed

QoE management of multimedia streaming services in future networks : a tutorial and survey

No embargo require

Design and Effect of Continuous Wearable Tactile Displays

Our sense of touch is one of our core senses and while not as information rich as sight and hearing, it tethers us to reality. Our skin is the largest sensory organ in our body and we rely on it so much that we don\u27t think about it most of the time. Tactile displays - with the exception of actuators for notifications on smartphones and smartwatches - are currently understudied and underused. Currently tactile cues are mostly used in smartphones and smartwatches to notify the user of an incoming call or text message. Specifically continuous displays - displays that do not just send one notification but stay active for an extended period of time and continuously communicate information - are rarely studied. This thesis aims at exploring the utilization of our vibration perception to create continuous tactile displays. Transmitting a continuous stream of tactile information to a user in a wearable format can help elevate tactile displays from being mostly used for notifications to becoming more like additional senses enabling us to perceive our environment in new ways. This work provides a serious step forward in design, effect and use of continuous tactile displays and their use in human-computer interaction. The main contributions include: Exploration of Continuous Wearable Tactile Interfaces This thesis explores continuous tactile displays in different contexts and with different types of tactile information systems. The use-cases were explored in various domains for tactile displays - Sports, Gaming and Business applications. The different types of continuous tactile displays feature one- or multidimensional tactile patterns, temporal patterns and discrete tactile patterns. Automatic Generation of Personalized Vibration Patterns In this thesis a novel approach of designing vibrotactile patterns without expert knowledge by leveraging evolutionary algorithms to create personalized vibration patterns - is described. This thesis presents the design of an evolutionary algorithm with a human centered design generating abstract vibration patterns. The evolutionary algorithm was tested in a user study which offered evidence that interactive generation of abstract vibration patterns is possible and generates diverse sets of vibration patterns that can be recognized with high accuracy. Passive Haptic Learning for Vibration Patterns Previous studies in passive haptic learning have shown surprisingly strong results for learning Morse Code. If these findings could be confirmed and generalized, it would mean that learning a new tactile alphabet could be made easier and learned in passing. Therefore this claim was investigated in this thesis and needed to be corrected and contextualized. A user study was conducted to study the effects of the interaction design and distraction tasks on the capability to learn stimulus-stimulus-associations with Passive Haptic Learning. This thesis presents evidence that Passive Haptic Learning of vibration patterns induces only a marginal learning effect and is not a feasible and efficient way to learn vibration patterns that include more than two vibrations. Influence of Reference Frames for Spatial Tactile Stimuli Designing wearable tactile stimuli that contain spatial information can be a challenge due to the natural body movement of the wearer. An important consideration therefore is what reference frame to use for spatial cues. This thesis investigated allocentric versus egocentric reference frames on the wrist and compared them for induced cognitive load, reaction time and accuracy in a user study. This thesis presents evidence that using an allocentric reference frame drastically lowers cognitive load and slightly lowers reaction time while keeping the same accuracy as an egocentric reference frame, making a strong case for the utilization of allocentric reference frames in tactile bracelets with several tactile actuators

Estimating Movement from Mobile Telephony Data

Mobile enabled devices are ubiquitous in modern society. The information gathered by their normal service operations has become one of the primary data sources used in the understanding of human mobility, social connection and information transfer. This thesis investigates techniques that can extract useful information from anonymised call detail records (CDR). CDR consist of mobile subscriber data related to people in connection with the network operators, the nature of their communication activity (voice, SMS, data, etc.), duration of the activity and starting time of the activity and servicing cell identification numbers of both the sender and the receiver when available. The main contributions of the research are a methodology for distance measurements which enables the identification of mobile subscriber travel paths and a methodology for population density estimation based on significant mobile subscriber regions of interest. In addition, insights are given into how a mobile network operator may use geographically located subscriber data to create new revenue streams and improved network performance. A range of novel algorithms and techniques underpin the development of these methodologies. These include, among others, techniques for CDR feature extraction, data visualisation and CDR data cleansing. The primary data source used in this body of work was the CDR of Meteor, a mobile network operator in the Republic of Ireland. The Meteor network under investigation has just over 1 million customers, which represents approximately a quarter of the country’s 4.6 million inhabitants, and operates using both 2G and 3G cellular telephony technologies. Results show that the steady state vector analysis of modified Markov chain mobility models can return population density estimates comparable to population estimates obtained through a census. Evaluated using a test dataset, results of travel path identification showed that developed distance measurements achieved greater accuracy when classifying the routes CDR journey trajectories took compared to traditional trajectory distance measurements. Results from subscriber segmentation indicate that subscribers who have perceived similar relationships to geographical features can be grouped based on weighted steady state mobility vectors. Overall, this thesis proposes novel algorithms and techniques for the estimation of movement from mobile telephony data addressing practical issues related to sampling, privacy and spatial uncertainty

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Towards Segment-level Video Understanding: Detecting Activities from Untrimmed Videos

We generate massive amounts of video data every day. While most real-world videos are long and untrimmed with sparsely localized segments of interest, existing AI systems that can interpret videos today often rely on static image analysis or can only process temporal information in a short video snippet. To automatically understand the content of long video streams, this thesis mainly describes the efforts to design accurate, efficient, and intelligent deep learning algorithms for temporal activity detection in untrimmed videos. Detecting segments of interest from untrimmed videos is a key step towards segment-level video understanding. Depending on the purposes of tasks being performed, we address three different activity detection tasks: detecting activities of interest from videos without specific purposes (i.e., temporal activity detection); detecting temporal segment that best corresponds to a language query (i.e., natural language moment retrieval); and detecting activities given less supervision (i.e., weakly-supervised or few-shot activity detection).In temporal activity detection, We first propose a highly unified single-shot temporal activity detector based on fully 3D convolutional networks, by eliminating explicit temporal proposal and classification stages. Evaluations show that it achieves state-of-the-art on temporal activity detection while being super efficient to operate at 1271 FPS. We then investigate how to effectively apply a multi-scale architecture to model activities with various temporal length and frequency. We propose three novel architecture designs: (1) dynamic temporal sampling; (2) two-branch feature hierarchy; (3) multi-scale contextual feature fusion, and we combine all these components into a uniform network and achieve the state-of-the-art on a much larger temporal activity detection benchmark.In natural language moment retrieval, we aim to localize the segment that best corresponds to a given language query. We present a language-guided temporal attention module and an iterative graph adjustment network to handle the semantic and structural misalignment between video and language. The proposed model demonstrates superior capability to handle temporal relations, thus, significantly improves the state-of-the-art by a large margin.Finally, we study the problem of weakly-supervised and few-shot temporal activity detection to mitigate the drawbacks of huge amounts of supervision needed to train a temporal detection model. Namely, we answer the question if we can learn a temporal activity detector under weak supervision that is able to localize unseen activity classes. A novel meta-learning based detection method is accordingly proposed by adopting the few-shot learning technique of Relation Network. Results show that our method achieves performance superior or competitive to state-of-the-art approaches with stronger supervision.In summary, we propose a suite of algorithms and solutions to automatically detect segments of interest in long untrimmed videos. We hope our studies could provide insights for researchers to explore new deep learning paradigms for future computer vision research, especially on video-related topics