Live Video Streaming

Kemajuan dalam kemudahan rangkaian pada masa kini telah meningkatkan penggunaan internet di seluruh dunia. Teknologi ICT telah dibangunkan dari semasa ke semasa mengikut tarikan pasaran juga dorongan teknologi. Adanya jalur lebar yang berkapasiti tinggi telah mendorong teknologi yang dipanggil penstriman media berkonsepkan penyediaan fail video di internet. Istilah penstriman video secara langsung juga di dalam konteks media langsung itu adalah satu proses mengekod dan dimampatkan dalam masa nyata. kajian ini di lakukan adalah untuk mengenal pasti dan menganalisis penerimaan teknologi live video streaming di kalangan pengguna internet di mana mewakili gen Y dalam usia 18 hingga 30. Kaedah yang telah digunakan iaitu teknologi model penerimaan (TAM) di dalam mengenal pasti hubungan antara pembolehubah.

In this paper, we address the aforementioned challenge by leveraging the crowdsourcing technique. Actually, considering individual diversity, mobile users usually enjoy different wire- less network conditions because of different network operators, different locations or even different smartphones. We observe that integrating multiple users’ available bandwidth can ef- fectively improve these users’ network conditions including network capacity and network volatility. Just as shown in Figure 1, two users A and B show different network condition at different times. When these two users do not cooperate with each other and only enjoy live video streaming by the single channel between local and cellular base directly, their available bandwidths are illustrated in the left subfigure (a). Here, we adopt the MAD (mean absolute deviation) 1 metric to quantitatively analyze the network volatility. User A’s network has a MAD value of 214 while B is 143. Therefore, the whole system’s MAD value is 214+143=357. Comparatively speaking, if there exists an intermediate node (broker) which can build connection with the cellular base and these two users simultaneously, the broker can then maintain the users’ individual cellular network condition information. Based on the information, the broker can aggregate these two users’ network resource and re-allocate/re-schedule network resource to the users by central control. In this case, the whole system’s MAD value is only 214 (as shown in right subfigure (b)). In this paper, we dig more deeply into this phenomenon and provide theoretical results to show the advantages of user cooperation through crowdsourcing brokerage, including larger capacity provisioning and lower network volatility.

In this scenario, we designed a Car that can operate wirelessly by Android mobile. This Research paper has represented how a Car controlled through Bluetooth and live video streaming which is operated through Wi-Fi by an Android application. Many papers focus on mainly Controlling Car through Bluetooth. But they did not provide any video streaming. [1] Some other papers focus on controlling car by Microcontroller only. The authors in paper [2] design a wireless car using an application without providing any concept of live video streaming. In paper [3], strongly focuses in IP Camera and Wi-Fi. In paper [4], the authors tried to find out modern technology of Bluetooth communication.

Most of currently deployed live video streaming systems don‟t consider heterogeneous bandwidth of peers. To address this issue, many researches combine P2P solution with scalable video streaming, however none of the existing hybrid CDN-P2P consider it. This paper presents the combination of Hybrid CDN-P2P and temporal scalable streaming. The main design goal of our approach is changing requesting and transmission policy of peers commensurate with bandwidth to optimize video distribution and frame losses. Heterogeneity is addressed by changing upload policy of peers to send different layers for neighbors. The experimental result shows that, it achieves significant improvement to decrees video distortion and hop count.

End-to-End Delay (EED), the required time in second for transferring a packet from the video source to a node, is the third metric which is measured by this study. This metric clearly shows the performance of a routing protocol in live video streaming, because it is very important to deliver video frames to receivers before their playback times. Otherwise, the probabilities of video distortion and payback skip event increases. As depicted in Figures 9 and 10, proactive protocol, which routes the traffic on the predefined paths, introduces the least amount of EED, whereas the HWMP imposes the highest. According to these figures, an increase in the number of Mesh nodes, while the number of STA nodes remains constant, can increase the amount of EED more than that of the case in which the number of STA nodes increases while the number of Mesh nodes remains constant. It is necessary to mention that 20 milliseconds end-to-end delay is reasonable and acceptable for video streaming over WMNs. Therefore, all routing protocols can vindicate this requirement. Figures 9 and 10 clearly show

Abstract- Android is one of most popular open source operating system for smart devices like phones, tablet, set-top box, Android TV, Android Auto, and Android Wear. Most of the Smart devices has hardware capable of video processing and wireless streaming. This paper explains streaming of live camera content from Android Wearable Device like Watch to Handheld Mobile Phone/Tablet device. This Streaming content used for variety of Application in day to day life. Android Smart Device Consume and produce Live Video streaming and share video to another device. Bluetooth is more cost-efficient and power-efficient wireless communication layer to transfer media content between the devices. Bluetooth, making it ideal for small, light mobile devices, but not suitable for traditional media encoding and Real-time transmission due to limited Bandwidth, High degree of error rates, and the time-varying nature of the radio link. The media streaming over Bluetooth stances many challenges. This paper explains the protocol for media transmission content for Bluetooth, Camera and Bluetooth configuration, compressing technique on Wearable devices.

The topology of p2p live video streaming involves virtual lines that connect peers over the application layer to transfer video data, with each peer acting as a server-client in the p2p network. The following conclusions are drawn from the review results. The tree topology is efficient in packet distribution, whereas the mesh topology incurs very long delays. The tree topology is sensitive to link and peer failure, whereas the mesh topology has a reliable behavior with dynamic alteration. The tree topology is usually designed for a large group with one source, whereas the mesh topology is usually designed for a large group with one or more sources. The properties and performance of the multi-tree topology lie between the tree and mesh topology. Tree and multi tree topology used push-up mechanism for data delivery, while mesh topology used pull-down mechanism. Finally, the multi- tree topology has close performance to directed mesh topology. Table I shows the summary of the three main types of the peer-to-peer topology for live video streaming.

Nowadays, more and more visitors are attracted to web locations for live video content which leads to sustainability issues when clients rise above the upload capabilities of the streaming server. Since IP Multicast failed to satisfy these requirements, in the last decade the science community intensively works in the field of P2P networking technologies for live video broad- cast. In this paradigm every user ( peer ) main- tains connections with other peers and forms an application level logical network on top of the physical network. Video stream originates at a source and every peer acts as a client as well as a server forwarding the received video packets to the next peer. Two types of data cir- culate in these networks. Control data is used for network formation and maintenance ( Con- trol Scheme ) , and video data is the disseminated

s repetitive behavior. In some cases, distance of the moving object also forms a basis for it to be considered a background, e.g. if in a scene one person is close to the camera while there is a person far away in background, in this case the nearby person is considered as foreground while the person far away is ignored due to its small size and the lack of information that it provides. Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of video frame

The research is significant and important from theoretical and practical perspectives. For example, high video quality on peers can be the most important issue when a lecturer is delivering a live speech to some students’ gadgets on campus. It is generally believed that live video streams have been one of the most important traffics over all computer networks, especially the Internet and wireless networks. The fact is that many people are eager to watch live or on-demand video streams using their gadgets (Saeed et al., 2013). Therefore, P2P video streaming over wireless mesh networks using random network coding has been of great interest for this purpose (Zhenyu et al., 2011).

The below presents the screenshots of the system. Here the control panel with control option to save the file which is used for capture of picture some change in video resolution. When some movement occur it analyse the incoming image and store important items, and here we can view the JPEG images and video will be played smoothly even we can watch on mobile with good reliable performance. While remotely can view in the 640x360 MJPEG image, the Raspberry Pi reports 67% CPU without overlocking.

The proposed system implements existing system also. In addition with proposing a novel cross-layer P2P scheme for live video streaming in MCANET, tree based multicasting option is also provided so that a single path consists of both Wired and Wireless connection. In addition, access gateways and area gateways are maintained so that a node can be reached using the correct area gateways in the dynamic network environment. Tree based architecture assists in efficient path identification and propagation. Using the architecture, Segments of single video resource can be transferred to a selected node from a set of nodes having those segments.

Streaming is a method for intelligent broadcasting of data on the network, it differs from conventional multimedia services because it isn't necessary to wait for the end of downloading video and able to start playing back. Current approaches in P2P video streaming can be classified as tree-based, mesh- based or hybrid. With tree-based model uses a push method to transfer data. This model has low start-up delay. However, there are two main problems in this method: if the bandwidth of parent node is low, children nodes will be lose data and when parent node failure, other nodes can’t receive data until completing the recovery of the tree. On the other hand, mesh- based model uses a pull method to request necessary data from a number of neighbor nodes. However, mesh-based model requires large buffers to support pull data from neighbors and there is an adjustment between minimum delay by sending pull request and overhead of whole system. So, both models have their own strengths and weaknesses. This paper proposes a new architecture system design for P2P live video streaming that combines the advantages of pull and push methods for broadcasting live video. This consists of two states: tree-based and mesh-based. Also, we design network topology with storage nodes are adjacent to the broadcaster, because they are more stable than streaming nodes which can leave system anytime. The remainder of this paper is organized as follows. We briefly discuss the related

Abstract— The new method has been proposed a novel idea for mobile video streaming and live streaming in the clouds. The user request for videos over the mobiles through wireless links this wireless links capacity cannot be corporate with the traffic demand. As gap between traffic demand and link capacity, with link conditions, result poor quality service and sending data on this channel result in buffering time. Mobile traffic is serious concern for mobile network operator to provide the qos to mobile user, this video traffic exceeds the video bandwidth capacity of cellular network, to address the delay, packet lose video traffic. This paper propose a new framework, bandwidth efficient mobile video and live streaming using Amazon web services(AWS) elastic computing cloud (ec2) called as AWS Cloud, to demonstrate video performance and cloud server for live streaming and adaptive video streaming in cloud. which has two main parts: adaptive mobile live video streaming (LVS) and adaptive video streaming (AVS). LVS and AVS construct a private agent to provide video streaming services efficiently for every mobile user. For a given user, LVS-AVS lets an agent adaptively adjust user streaming flow with a scalable video coding technique based on the feedback of link quality. Sharing the video based on url path of video in cloud for social networking. The framework shows that the private agents in the clouds can effectively provide the adaptive streaming for both live visuals and video in cloud.

In this paper, proposed a advanced traffic signalling system using machine learning,To design and implement Cautions System for live video streaming by using Background subtraction technique with SVM algorithm which is useful to provide security where it is necessary.This trained SVM is the core of a human detection algorithm which searches optical flow images for human-like motion patterns. A SVM builds a set of hyper planes in a high or multi-dimensional space can be used for regression and classification. High quality of the separation is attained by generating a functional- margin which provides the greatest distance between the nearest training input data points.

One of the main challenges in video transmission is understanding and adapting to the varying network bandwidth. The traditional approach of bandwidth estimation is not accurate as there are many factors like congestion that can delay the arrival rate of the ping packet which may lead to a misconception that the bandwidth was low. Thus, the better approach to this problem will be to estimate the link conditions based on the buffer fullness. In this paper, a new system to support streaming of live and stored video through wireless network is proposed which is based on adaptive playback buffer management on the top of HTTP at the client. The buffer fullness is treated as a direct state variable that reflects the fluctuation of the network bandwidth. The buffer fullness estimation predicts the buffer status at a point in the future based on observations of the buffer over a stipulated period of time. The proposed algorithm uses non-linear exponential non-parametric regression for computing the decision parameter. A feedback message is then sent to the server in order to change the quality of the video stream for smoother video play at the client side. The synchronized update and feedback between the server and clients is achieved using HTTP protocol. During the experimentation with live video streaming, the proposed algorithm shows an improvement of 24.48% in average peak signal-to-noise ratio and 6.63% in average structural similarity index against the buffer underflow probability algorithm.

Currently, there are many peer-to-peer (P2P) application layer multicast (ALM) solutions which offer a serious alternative to IP multicast and content delivery networks live video streaming applications. Nonetheless these P2P infrastructures suffer from Quality of Service (QoS) problems due to several causes: dynamics of users’ presence, selfish behavior, latency, bandwidth and geographic distance. Several research works address these problems individually, but none of them provides a global solution. In this paper, we show how a solution based on a multi-agent system can solve this problem and the challenges it has to face. Keywords: P2P IPTV, Multi-Agent systems, Overlay Networks, Live Video Streaming, Quality of Service.

The paradigm shift of network design from performance-centric to constraint-centric has called for new signal processing tech- niques to deal with various aspects of resource-constrained communication and networking. In this paper, we consider the com- putational constraints of a multimedia communication system and propose a video adaptation mechanism for live video streaming of multiple channels. The video adaptation mechanism includes three salient features. First, it adjusts the computational resource of the streaming server block by block to provide a fine control of the encoding complexity. Second, as far as we know, it is the first mechanism to allocate the computational resource to multiple channels. Third, it utilizes a complexity-distortion model to determine the optimal coding parameter values to achieve global optimization. These techniques constitute the basic building blocks for a successful application of wireless and Internet video to digital home, surveillance, IPTV, and online games.

Metadata based high Quality Video Streaming using Curvelet Transform is proposed for multimedia services. Here, for streaming high resolution video is step down as low resolution video. During full screen view the quality of video is diluted. To maintain information with the same quality, metadata is extracted from HR video that will send along with LR profile. Figure 1 depicts the transmitter section of the proposed work and receiver section is constructed symmetrically.

The basic working of the fencing is as follows. Photovoltaic energy from the sun is absorbed with the help of solar panels which are made up of photovoltaic cells. These photovoltaic (PV) cells are used to convert solar energy to electrical energy. This energy is stored in batteries through charge controller during the day time in order to be utilized whenever required. The Inverter produces square wave pulses. This square wave is stepped up to high voltage level and can be connected to the fence. This way the fence is electrified and animals touching the fence receive the shock. Due to high voltage shock to the animals touching the fence, animals keep away from the fence and field is protected. A DC 12V supply from solar panel is adjusted to get 5v using 7805 voltage regulator. This 5v signal turn on the raspberry pi(IOT devices)[7] to our mobile through mobile hotspot. Once the Raspberry PI is connected to our mobile then we need to check the VNC viewer app which is installed in our mobile in which we get the desktop of raspberry pi which is far away from us. This desktop contains all the features of PC. So it acts as a mini computer in our mobile. From the raspberry pi we will connect the PIR sensor which will act as motion detector. PIR sensor will give the signal to the Raspberry PI as an alert to our mobile; hence we need to go for Blynk app which will communicate the animal detected signal from raspberry pi to our mobile. Once the signal is obtained then we will be able to turn on the PI Camera which is connected to Raspberry PI by giving the command signal in the VNC viewer server then pi camera will be on and it will give the live streaming of the animal in VLC media player. Depending upon the animal, farmer will be able to produce the frightening sounds through VNC viewer which will divert the animals. Even though the animal tries to enter in to the field the electrical fence will give the shock to them but it does not cause any death to it. So by using the above system we successfully monitor and protect our crops even from remote areas which is very helpful to the farmers.