Spiking Neural Networks (SNNs) have gained increasing attention due to their potential for low-power computation on neuromorphic hardware. A widely adopted training strategy for SNNs is direct coding, which enable backpropagation on neuron implementations using continuous-valued surrogate activations. However, recent studies have shown that direct-coded SNNs remain substantially less energy-efficient than their event based counterparts, limiting their practical deployment in energy sensitive scenarios. Still, to promote the reusability of pretrained SNN database on direct code, this motivates an important yet underexplored question: How can a SNN pretrained with direct code be effectively converted into an event-based representation? In this research, we present the first systematic investigation into this transfer problem, analyze the key challenges that arise when transitioning from direct-coded to event-based computation and propose a set of methods to enable energy-efficient transfer while preserving model performance.

Point cloud video streaming over networks is challenging because of the high data rate of uncompressed point cloud data. Adaptive point cloud video streaming has been proposed to deal with this challenge. However, temporal quality variation and stalling might occur under unstable network conditions, potentially degrading users’ Quality of Experience (QoE). This paper aims to evaluate and model the impacts of temporal quality variations and stalling on users’ QoE in adaptive point cloud video streaming. We first conduct a large-scale subjective study to construct a QoE database. Then, based on the constructed database, the effects of individual factors are analyzed, and two novel QoE prediction models are presented. Experiment results show that the proposed QoE models achieve high prediction performance in PLCC, SROCC, and RMSE across various point cloud videos.

Recent years have witnessed the rising of short-form video platforms such as TikTok. Apart from conventional videos, short-form videos are much shorter and users frequently change the content to watch. Thus, it is crucial to have an effective streaming method for this new type of video. In this paper, we propose a resource-efficient prefetching method for short-form video streaming. Taking into account network throughput conditions and user viewing behaviors, the proposed method dynamically adapts the amount of prefetched video data. Experiment results show that our method can reduce the data waste by 37$\sim$52% compared to other existing methods.

HTTP Adaptive Streaming has become a popular solution for multimedia delivery nowadays. However, due to network bandwidth fluctuations, video quality strongly varies during streaming. Therefore, a key challenge in HTTP Adaptive Streaming is how to evaluate the overall quality of a streaming session. In this article, a machine learning approach is proposed for overall quality prediction, where each segment in a streaming session is represented by a set of features. Two options of the feature set are investigated. In the first option, we use four features, namely segment quality, content characteristics, stalling duration, and padding. The second option consists of three features, namely bitstream-level parameters, stalling duration, and padding. The features are fed into a Long Short Term Memory (LSTM) network that is capable of exploring temporal relations between impairment events of quality variations and stalling events. The overall quality is predicted from the outputs of the LSTM network using a linear regression module. Through experimental results, it is shown that the proposed approach achieves a high prediction performance and outperforms seven existing approaches. Especially, the second option is found to be both efficient and effective. The source code of the proposed approach has been made available to the public.

HTTP Adaptive Streaming has become the de facto choice for multimedia delivery. However, the quality of adaptive video streaming may fluctuate strongly during a session due to throughput fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this article, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify four important components of the cumulative quality model, namely the minimum window quality, the last window quality, the maximum window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. Our subjective dataset as well as the source code of the proposed model have been made publicly available at https://sites.google.com/site/huyenthithanhtran1191/cqmdatabase .

360-degree video is the main content type of Virtual Reality, providing users with immersive viewing experience. In this paper, we propose a novel adaptation method for 360-degree video streaming over HTTP/2, which can provide high viewing experience to users under time-varying network conditions and time-varying user head movements. The proposed method utilizes Scalable Video Coding to solve the trade-off between network adaptivity and user adaptivity. An optimal tile layer selection algorithm is provided. To cope with sudden throughput drops, the delivery of late layers is terminated using HTTP/2's stream termination feature. Also, a tile layer updating scheme is proposed to deal with viewport estimation errors. Experimental results show that the proposed method can improve the average bitrate of viewport by 16-17% compared to a reference method.

Recently, TCP SYN flood has been the most common and serious type of Distributed Denial of Service attack that causes outages of server resource of Internet Service Providers. In another aspect, Software Defined Networking (SDN) has emerged as a new networking paradigm to increase network agility and programmability. SDN is also a promising architecture to deal with the network security issue where we can flexibly change security rules and control incoming flows. In this article, we design an Openflow/SDN network remedy to combat specifically TCP SYN flood. We show security threats for the SDN architecture and exploit SDN capabilities and features to design a SDN-based SYN Proxy (SSP) paradigm to mitigate such TCP SYN threats. Our SSP is proved to be a network-based solution to protect application servers in terms of decreasing number of Half-Open Connections at an application server and increasing probability of successful establishment for a TCP flow connection under TCP SYN Flood attack. Using SSP to support application servers is shown to outperform the case where the servers adopt only the protection scheme of Microsoft Windows server reference model without utilizing SSP. SSP also shows that it can reduce the time a flow entry occupies the switch resource by 94% in comparison with the Avant-Guard solution. In addition, SSP improves the successful connection rate and average connection retrieval time in comparison with the standard Openflow solution.

The quality of adaptive streaming may fluctuate strongly during a session. Therefore, a challenge is how to evaluate the quality of a session over time. In this study, a cumulative quality model is proposed for HTTP adaptive streaming. The model is based on the concept of a sliding window of video segments over a session. It is found that the minimum window quality, the last window quality, and the average window quality are key components of the cumulative quality model. In addition, the proposed model is applicable to real-time quality monitoring, which is especially important for cost-effective evaluation of streaming technologies and as basis for adaptive quality mechanisms.

HTTP adaptive streaming (HAS) has become popular for delivering videos over the Internet. However, most HAS services are currently based on the traditional HTTP/1.1, which has limitations for live streaming. Meanwhile, the new HTTP/2 was standardized in 2015 with many improvements. In this letter, we present a novel HTTP/2-based adaptation scheme for low-delay live streaming over mobile networks. The proposed scheme leverages two HTTP/2 features called server push and stream termination to enable a shorter camera-to-display delay. Experimental results with small buffer sizes show that our scheme can provide users with high video bitrate and stable buffer level. Furthermore, the buffer size in live streaming can be reduced to two sec without sacrificing video quality.

Currently, more and more 360-degree videos (or 360 videos for short) are being provided via the Internet. This kind of videos can render a virtual reality (VR) environment via a head-mounted display (HMD). However, understanding the quality of experience (QoE) of 360 videos is a big challenge because user experience in VR is a very complex phenomenon. In this paper, the QoE of 360 videos is considered in terms of four aspects, namely perceptual quality, presence, acceptability, and cybersickness. Subjective tests are designed to investigate the influences of important factors including encoding parameters, content characteristics, and device types on QoE aspects. In addition, a comparison of perceptual quality and acceptability between VR and non-VR rendering modes, which refer to watching 360 videos with and without using an HMD respectively, is also made in this study. To the best of our knowledge, this is the first study that covers these four QoE aspects and a large number of influence factors.

HTTP streaming has become a cost effective means for video delivery nowadays. To enable adaptivity to networks and terminals, a provider should generate multiple representations of an original video as well as the related signaling metadata. So far, most previous studies have just focused on the case of CBR (constant bitrate) video. In this paper, we propose a novel adaptation method for VBR (variable bitrate) video streaming. Based on a trellis representation to estimate future buffer levels, the proposed method can provide smooth video quality while avoiding buffer underflows. The experimental results show that our approach can perform effectively under drastic changes of both connection throughput and video bitrate.

Due to the heterogeneous nature of networks and end-systems in distributed multimedia systems, multimedia applications should ideally be designed to counteract fluctuations in network bandwidth and end-system processing capacities for providing end users a certain degree of quality of service (QoS). This requirement can be satisfied with scalable applications. In addition, with the current evolution in run-time reconfigurable computing, run-time reconfigurable multimedia platforms are becoming increasingly viable. In this paper, an end-to-end delivery chain framework for mapping scalable networked multimedia applications on reconfigurable platforms is presented. The framework is demonstrated by a case study of a 3D game running on a prototype run-time reconfigurable platform.

Terminal quality of service is the process of optimally scaling down the decoding and rendering computations to the available processing power, while maximizing the overall perceived quality. This process is applied in a real-time three-dimensional (3-D) decoding and rendering engine, exploiting scalable MPEG-4 coding algorithms. We derive a relation between the quality of the 3-D rendered objects and their processing requirements, expressed by simple CPU time models. The performance dependency parameters are in direct relation to the high-level 3-D content characteristics (number of triangles, number of rendered screen pixels per object) and are calibrated for the platform under test. Examples show that, for any pre-established frame rate, the platform workload can reliably be anticipated for adjusting the content and process parameters accordingly.