Hongbo Huang · The Open Automation and Control Systems Journal · 2015
With the development of wireless communication technology, video communication over the mobile ad hoc networks has been more and more applied. How to ensure the QoS of video communication is an important problem that should be solved quickly. In this paper, we propose a QoS routing algorithm for Video Streaming in MANET. The algorithm introduces multi-constrained QoS routing mechanisms, and optimizes paths through bandwidth, delay, service priorities and congestion degree constraints. The simulation results show that, in medium speed or low-speed MANET where nodes are concentrated, this algorithm can perform well in respects like extending the life of the nodes, reducing packet delay and improving packet delivery fraction.
Cited by 4Open access
Nouha Samet, Asma Ben Letaïfa, Mohamed Hamdi et al. · 2017
Video streaming services over the Internet are being widely used in mobile networks. Currently, service providers are relying more and more on securing data transferred between the service provider, namely the video streaming server, and the end user. However, securing video content includes at least the use of video encryption algorithm requiring processing and memory capabilities, provoking more delay and energy consumption. In any other context, energy consumption may not be a very important issue, but when using a mobile device, it will certainly become one. In this paper, we are dressing a comparison between different symmetric encryption algorithms, in terms of energy consumption, while decrypting and viewing a video stream on mobile device.
Cited by 2
Grochal, Anna · Jagiellonian University Repository (Jagiellonian University)
Problemem przedstawionym w tej pracy jest implementacja i ewaluacja algorytmu, który pozwoli uzyskać dokładne wyniki dla strumieniowej klasteryzacji danych. Podczas pracy ze strumieniami danych możliwe jest, że klastry będą migrować z czasem, mogą się nakładać, a następnie wracać do swoich poprzednich pozycji. Niektóre nowe klastry mogą się pojawić, a inne mogą zniknąć.Proponowany algorytm powinien zapewniać sposób na zapamiętanie wcześniej wykrytych klastrów w celu rozpoznania ich jako oddzielnych klastrów przez całą ich podróż. Zaimplementowany algorytm wykorzystuje algorytm DBSCAN w fazie offline.Ta praca zawiera wyjaśnienie, czym jest klastrowanie, kilka przykładów algorytmów stosowanych do klastrowania klasycznych zbiorów danych, czym są strumienie danych i jakie są wyzwania związane z klastrowaniem danych strumieniowych. W kolejnych rozdziałach znajduje się wyjaśnienie idei nowego algorytmu - DBSCANStream, jego kod oraz przykłady klastrowania z jego wykorzystaniem. Ocenę algorytmu przeprowadzono za pomocą metryk Rand Index i V-measure.
Cited by 0
Öge, Ahmet · OpenMETU (Middle East Technical University) · 2019
HTTP Adaptive Streaming (HAS) is a popular video streaming method where the client downloads video segments over standard HTTP protocol. In HAS, the server stores the video segments that are encoded in different qualities which determine the video bit rates. To this end, the client first downloads a file which describes the video segments. Then, using a rate adaptation algorithm, the client decides on the most appropriate video bit rate for the next segment to download and sends an HTTP request for that segment. The rate adaptation algorithm utilizes measurements of the network bandwidth by dividing the previously downloaded segments’ sizes by their download times. HAS exploits that HTTP is an ubiquitous application layer protocol which can easily pass any network device, firewall and Network Address Translation. Video streaming performance is measured by the user’s perception that is quantified by Quality of Experience (QoE). Accordingly, video freezes must be avoided as they decrease QoE significantly. The client aims for downloading at the highest quality utilizing the available bandwidth as much as possible. However, if the requested bit rate is increased too much, delays and packet loss events drive the client to decrease the bit rate subsequently. Such frequent rate switches decrease the QoE. Furthermore, it is desired that fairness among the clients is preserved where the clients that stream over a common bottleneck link share the bandwidth fairly. In this thesis, we provide an Efficient and Fair Adaptive STreaming (EFAST) architecture to improve the performance of HAS according to the performance metrics that are defined above. In this architecture, clients rate adaptation is implemented by using a Fuzzy Logic Controller. The inputs of EFAST Fuzzy Logic Controller are the receiver buffer size and the estimated bandwidth. After fuzzy control steps, it selects a proper video bit rate of next segment. An analytical model of rate adaptation algorithm is defined to show that EFAST achieves the desired bit rate and buffer occupancy. We implement EFAST in both simulation environment and in real life network. We then perform experiments that evaluate the performance of EFAST in comprehensive network scenarios. Furthermore, we compare EFAST to other wellknown HAS rate adaptation algorithms. Our results show that EFAST has more fairly bandwidth allocation among clients who share bottleneck, low switch rate changes, and high bottleneck efficiency with no buffer depletion.
Cited by 0Open access
Andrés Merino, Jaime · QRU Quaderns de Recerca en Urbanisme · 2015
This master thesis presents a novel stream clustering algorithm, called StreamLeader. It presents a way to deliver clustering without the need of resorting to conventional clustering algorithms, like most other algorithms do. We test it, outperforming its state of the art rivals in most of the cases
Cited by 0Open access
Zhiwen Xu, Xiaoxin Guo, Zhengxuan Wang et al. · Lecture notes in computer science · 2005
Cited by 1
Zhiming Zhang, Jin Zhou, Zhen Chen et al. · Journal of Software · 2012
在对等网(peer-to-peer,简称P2P)流媒体系统中,节点(用户)的输出带宽(上行带宽)容量利用率的提高能够降低服务器的带宽开销.网络编码可以实现组播的最大吞吐率,因而具有提高系统中节点输出带宽容量利用率的潜力.将随机线性网络编码应用到P2P 流媒体系统中,建立了基于随机线性网络编码的P2P 流媒体传输过程模型,并据此建立传输算法的优化模型,比较研究了贪婪式算法、最少者优先算法和随机算法等.优化结果表明,随机算法可以平等均匀地获取数据包,能够最充分地利用节点的输出带宽容量,降低服务提供商的运营成本.通过对优化模型解的分析,对实际系统中的传输算法给出了设计指导原则.
Cited by 0Open access
Sorin Faibish, Dominique Cote, Xiaoye Jiang · 2010
This poster introduces a new distributed caching algorithm used by the installable Celerra MPFS file system client to allow streaming high throughput High-Definition video streams. Large disk latencies in high throughput streaming result in a high video frame drop rate. Dropped frames rate increases with the video throughput. The algorithm proposed in this paper uses new caching ideas inspired by signal processing techniques to address dropped frames. Our techniques remove the dropped frames effect entirely, while optimizing the aggregate throughput of the storage array.
Cited by 0
Duc–Tan Tran, Canh V. Pham, Dung Pham Trung et al. · Lecture notes in computer science · 2024
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Vink, Ruben · TU/e Research Portal
Cited by 0
Gabriel-miro Muntean · 2007
Abstract — This paper proposes a TCP compatible greediness control mechanism that tunes the greediness of the multimedia streaming process based on client priority, in order to make more efficient use of the wireless network and increase the overall user perceived quality. The majority of streaming solutions use rate adaptation based on congestion avoidance mechanisms that try to obtain as much bandwidth as possible from the limited network resources. However the lack of both knowledge about the characteristics of target devices and cross-layer communication results in fair bandwidth distribution at the transport layer, but creates unfairness at the application layer. This unfairness mostly affects user perceived quality when streaming high quality multimedia. Therefore, there is a need to allow application layer streaming applications tune the aggressiveness of transport layer congestion control mechanisms, in order to create application layer Quality of Experience fairness between competing media streams, by taking their device characteristics into account. I.
Cited by 0
YANG Ge1, FAN Xiu-mei3 · SHILAP Revista de lepidopterología · 2009
The scheme that the patch bytes were segmented and cached was employed.The cache window size was up-dated periodically according to the popularity of streaming media object.The principle was obeyed that the data cached for each streaming media object were in proportion to their popularity at the proxy server.Simulation results show that the strategy is more adaptive than MBP（multicast batched patching） algorithm and OBP（optimized batch patch-ing）+prefix ＆ patch caching algorithm.It can significantly reduce patching data through patching channel under the cir-cumstance of the same maximum cache space.It can effectively reduce the server load and network bandwidth usage on backbone link.It can also save the transmission cost.
Cited by 0Open access
Zoltán Király, Erika R. Bérczi‐Kovács · 2011
Multi-layered video streaming considers different quality requirements of the receivers. Network coding has been shown to be a useful tool to increase throughput of multi-layered service compared to simple multicasting. Kim et al. gave a simple effective algorithm using network coding. We generalize their approach and give an algorithm that solves the problem for two layers optimally for certain natural objective functions and prove NP-hardness of the problem for some other objectives, as well as for more than two layers. We also give some new heuristics for three layers.
Cited by 7
Yingjian Zhi, Na Wang, Binqiang Wang · JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY · 2011
摘要: 批处理是一种简单有效的流媒体调度机制。通过把批处理调度归结为平均访问延迟最优化问题，得到影响批处理算法性能的关键因素。基于分析结果，本文提出一种最大紧迫度优先的批处理机制及其实现方案。紧迫度的定义考虑了等待队列的多个属性。一旦有空闲信道时，该机制选择具有最大紧迫度的视频队列进行服务。详细的仿真结果证明了所提方案的有效性和可行性。 关键词: 流媒体 / 调度算法 / 紧迫度 / 批处理
Cited by 0Open access
Bradford Pielech · Digital WPI · 2004
Many modern applications process queries over unbounded streams of data. These applications include tracking financial data from international markets, intrusion detection in networks, monitoring remote sensors, and monitoring patients vital signs. These data streams arrive in real time, are unbounded in length and have unpredictable arrival patterns due to external uncontrollable factors such as network congestion or weather in the case of remote sensors. This thesis presents a novel technique for adapting the execution of stream queries that, to my knowledge, is not present in any other continuous query system to date. This thesis hypothesizes that utilizing a single scheduling algorithm to execute a continuous query, as is employed in other state-of-the-art continuous query systems, is not sufficient because existing scheduling algorithms all have inherent flaws or tradeoffs. Thus, one scheduling algorithm cannot optimally meet an arbitrary set of Quality of Service (QoS) requirements. Therefore, to meet unique features of specific monitoring applications, an adaptive strategy selector guidable by QoS requirements was developed. The adaptive strategy selector monitors the effects of its behavior on its environment through a feedback mechanism, with the aim of exploiting previously beneficial behavior and exploring alternative behavior. The feedback mechanism is guided by qualitatively comparing how well each algorithm has met the QoS requirements. Then the next scheduling algorithm is chosen by spinning a roulette wheel where each candidate is chosen with a probability equal to its performance score. The adaptive algorithm is general, being able to employ any candidate scheduling algorithm and to react to any combination of quality of service preferences. As part of this thesis, the Raindrop system was developed as exploratory test bed in which to conduct an experimental study. In that experimental study, the adaptive algorithm was shown to be effective in outperforming single scheduling algorithms for many QoS combinations and data arrival patterns.
Cited by 0Open access
Xizhi Hu, Zheng Wei, Wenchao Zhou · 2021
To achieve safe driving of vehicles, it is necessary to perceive information about the vehicle's surroundings, and computer vision is one of the key technologies to solve this problem. The YOLO series and SSD, RetinaNet algorithm are representative of one-stage target detection algorithms, which have high accuracy and high speed. YOLOv4 is the latest algorithm of YOLO series, which has improved the speed and accuracy of vehicle target detection than before, but there is still a distance from the real real-time in vehicle detection. This paper proposes an improved YOLOv4-based video stream vehicle target detection algorithm to solve the problem in the detection speed which is not fast enough. This paper first introduces the YOLOv4 algorithm theoretically, then proposes an algorithmic process to speed up the detection speed, and finally conducts practical road experiments. From the experimental results, the algorithm of this paper can improve the detection speed of the algorithm without losing accuracy, which can provide a basis for decision making for safe vehicle driving.
Cited by 23
Bei Yang, Houkuan Huang, Zhihai Wang · 2007
Data stream is a new data model that has recently attracted attentions in numerous applications. Considering the continuity, limitlessness of streaming data, this paper proposes a novel method to build a synopsis data structure, Nord_Histogram, for storing streaming data summary and a one-pass approximate algorithm, NHQC, for quantile computation. The algorithm implements quantile queries over data stream with the time and space requirements being linear with the number of buckets, beating several previous synopsis structure and algorithms in terms of time and space costs which grow at least logarithmically with the length of data stream. The correlation between computation error and main memory requirement is also analyzed. Experiment results show the good performance of NHQC with desirable accuracy and efficient time and space requirements.
Cited by 1
Waqas ur Rahman, Kwangsue Chung · 2018
In this paper, we propose a rate adaptation algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the video bit rates based on the estimated throughput and playback buffer occupancy. The performance of the algorithm is compared with that of throughput- and buffer-based adaptation algorithms. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network throughput to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that the proposed algorithm performs well regardless of the buffer size and segment duration.
Cited by 0
Saba et al. · SHILAP Revista de lepidopterología · 2019
The most popular medium that being used by people on the internet nowadays is video streaming. Nevertheless, streaming a video consumes much of the internet traffics. The massive quantity of internet usage goes for video streaming that disburses nearly 70% of the internet. Some constraints of interactive media might be detached; such as augmented bandwidth usage and lateness. The need for real-time transmission of video streaming while live leads to employing of Fog computing technologies which is an intermediary layer between the cloud and end user. The latter technology has been introduced to alleviate those problems by providing high real-time response and computational resources near to the client at the network boundary. The present research paper proposes priority weighted round robin (PWRR) algorithm for streaming operations scheduling in the fog architecture. This will give preemptive for streaming live video request to be delivered in a very short response time and real-time communication. The results of experimenting the PWRR in the proposed architecture display a minimize latency and good quality of live video requests which has been achieved with bandwidth changes as well as meeting all other clients requests at the same time
Cited by 0Open access
Reza Ahmari, Ahmad Mohammadi, Vahid Hemmati et al. · 2025
Dynamic clustering is a critical task for applications involving streaming data, such as customer behavior analysis and real-time pattern recognition. Traditional clustering algorithms like KMeans struggle to adapt to evolving data distributions, especially in non-convex or noisy environments. This paper presents a Genetic Algorithm (GA)-based dynamic clustering framework designed to handle such challenges effectively. Our method encodes cluster centers as chromosomes and employs a fitness function that minimizes the Sum of Squared Errors (SSE) while penalizing unassigned clusters. We evaluate the proposed method on both synthetic datasets (e.g., moons and Gaussian blobs) and a real-world customer dataset. Experimental results demonstrate that our GA-based approach outperforms KMeans in adaptability and robustness across time steps, with visual evidence and fitness score comparisons. The proposed system highlights the potential of evolutionary algorithms in adaptive learning scenarios, particularly in dynamic data stream environments.
Cited by 1