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Video Streaming using MPTCP and Efficient Real Time Adaptive Algorithm
Abstract: In this paper a real time adaptive algorithm is used for video streaming. Clients are becoming increasingly important due to the rapid growth of video traffic on wireless networks. Utilize multiple links to improve video streaming. In order to maintain high video streaming quality, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of service (QoS) requirements for video traffic. The advancements in wireless communication technologies prompt the bandwidth aggregation for mobile video delivery over heterogeneous access networks. Multipath TCP (MPTCP) is the transport protocol recommended by IETF for concurrent data transmission to multihomed terminals. However, it still remains challenging to deliver user-satis?ed video services with the existing MPTCP schemes because of the contra- diction between energy consumption and received video quality in mobile devices. To enable the quality- guaranteed video streaming, this paper presents an Efficient Real Time Adaptive Algorithm based solution. Wireless communication networks are featured with heterogeneity where multiple wireless technologies exist together. In the intersection of coverage areas of these different technologies, receiver having multiple interfaces can access them concurrently so as to improve the performance and this prompts bandwidth aggregation. A larger logical link can be created by aggregating low bandwidth links. The same link can be used by a multimode terminal for applications that are demanding high- bandwidth. In proposed work will work on Certificate less Key Management concept for maintain privacy and security to maintain the video aggregation stability, Bandwidth aggregation.

Keywords: Markov decision process, video streaming, Adaptive Video Streaming, Multipath TCP quality-awareness, heterogeneous wireless networks.

Video streaming is a type of media streaming in which the data from a video file is continuously delivered via the Internet to a remote user. It allows a video to be viewed online without being downloaded on a host computer or device. Video streaming works on data streaming principles, where all video file data is compressed and sent to a requesting device in small chunks. Video streaming typically requires a compatible video player that connects with a remote server, which hosts a prerecorded or pre-stored media file or live feed. The server uses specific algorithms to compress the media file or data for transfer over the network or Internet connection.
The size of each data stream depends on various factors, including actual file size, bandwidth speed and network latency. In turn, the user or client player decompresses and displays the streamed data, allowing a user to begin viewing the file before the entire video data or file is received.

Fig1. Live Video Streaming Works
II. Video Streaming Broad Classification
Video Delivery via File Download
Downloading is the transmission of a file from one computer system to another. From the Internet user’s point-of-view, to download a file is to request it from another computer (or from a Web page on another computer) and to receive it.

The File Transfer Protocol (FTP) is the Internet protocol for downloading and uploading files. For example TCP as the transport layer or FTP or HTTP at the higher layers. If you have a slow connection, then you can keep the download going for hours if need be, in order to get the file.

One of the most important things to remember when downloading movies is that they might be really large. Although it’s common for movie downloads to stay under 5 GB, some of the super high-definition videos might require 20 GB of space or more. Before downloading a movie, check that you have enough free space. You might end up having to store the movie on a different hard drive like a flash drive or external hard drive. It’s also possible that your internet connection simply doesn’t support fast downloads. For example, if you pay your ISP for a 2 MB/s download speed, you can download a 3 GB movie in around 25 minutes.

Video Delivery via Streaming
Video streaming is a type of media streaming in which the data from a video file is continuously delivered via the Internet to a remote user. It allows a video to be viewed online without being downloaded on a host computer or device. Video streaming works on data streaming principles, where all video file data is compressed and sent to a requesting device in small chunks.
Video delivery by video streaming attempts to overcome the problems associated with file download, and also provides a significant amount of additional capabilities.

Video streaming works by breaking a video into small chunks and sending them via the net to get reassembled and played at their final destination. As opposed to downloads, it transmits data as a continuous flow, so you can watch or listen almost immediately. In fact, streaming files can be hard to save. They disappear as soon as you’re done.

So if you want to get an idea of a video streaming website architecture, imagine a three-layered pie. The top layer is the client software, the bottom – the server component, and in the middle, the distribution component.

The server component takes the segmented chunks of stream, dresses them into a suitable format and prepares for distribution.

The distribution component delivers the media and associated resources to the client by request. For large-scale distribution, edge networks or other content delivery networks can also be used.

The client software determines the video to request, continually downloads and reassembles the streaming chunks of content back in a digestible format.
Advantages are Content is served in a way that allows files to play almost immediately after the file begins to download. Allowing your Web site visitors to download video files — especially copyrighted material — makes it much easier for your content to be pirated. Streaming video technology is harder to copy and prevents users from saving a copy to their computer if you don’t want them to.

The Author Ekaterina Pakulova ,Konstantin Miller,Adam Wolisz1, introduced an energy- distortion-aware MPTCP (EDAM) solution. Two transport protocols that are able to make use of multiple transmission paths simultaneously. Examples of such protocols include Multipath Real-Time Transport Protocol (MPRTP) and MUltipath Trans­ mission Control Protocol (MPTCP). MPTCP offers reliable connection-oriented transmission and is thus the protocol of choice for data transfer, but also for Video on Demand (VoD) streaming or live streaming with moderate latency, that are nowadays increasingly leveraging Hypertext Transfer Protocol (HTTP) as the application layer protocol. Paper talks about MPRTP. The approach consists of two components: Sender-Side Path Scheduling (SSPS) and Sender-Side Video Adaptation (SSVA) whose joint operation aims at increasing the QoE of the user by maximizing the video bit rate, minimizing the number of corrupted frames, and minimizing the amount of playback interruptions.

The Author Jiyan Wu 1, introduced an energy- distortion-aware MPTCP (EDAM) solution. The goal of the proposed EDAM scheme is to minimize the energy consumption while achieving the target video quality. the key contribution of EDAM is in the ?ow rate allocation. In the sender side, the decision making blocks include the parameter control unit, ?ow rate allocator, and retransmission controller. The parameter control unit collects the feedback information from the receiver side and estimates the input parameters (e.g., round trip time, available bandwidth, etc.) for the proposed algorithms. If there is packet drop detected by timeout or NAK (negative acknowledgement), the packet retransmission (Section IV.C) and buffer control (Section IV.D) algorithms will respond to such loss events. Due to the path asymmetry in heterogeneous wireless networks, the packets may arrive at the destination out-of-order. These packets will be reordered to restore the original video traf?c.

Energy Consumption Model: We employ the energy models developed in existing studies to characterize the energy consumption of mobile devices. These models consider the ramp, transfer and tail energy. The device-speci?c energy and power consumptions are pro?led in units of Joule (J) with the input parameters of signalling frequency, packet size and data transfers. Then, the total energy consumption for the given rate allocation vector across all the communication paths can be expressed as extensive measurements. Studies reveal that the energy consumed for transmitting the same amount of data through Wi-Fi network is lower than that across 3G (e.g., WCDMA) and LTE.

The Author Min Xing , Siyuan Xiang, and Lin Cai 2, introduced the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of service (QoS) requirements for video traf?c, such as the startup latency, playback ?uency, average playback quality, playback smoothness and wireless service cost. Furthermore, progressive download typically does not support transmitting video data over multiple links. To overcome the above disadvantages of progressive down- load, dynamic adaptive streaming over HTTP (DASH) has been proposed. In a DASH system, multiple copies of pre-compressed videos with different resolution and quality are stored in segments. The rate adaptation decision is made at the client side. For each segment, the client can request the appropriate quality version based on its screen resolution, current available bandwidth, and buffer occupancy status.

1) Stringent QoS requirements. High-quality live video streaming is bandwidth-intensive and delay-sensitive.The throughput demand for high-de?nition video distribution mainly ranges from 2?6 Mbps. Besides, the one-way delay is limited to be less than 150 ms to achieve excellent real-time video quality 5.
2) Network bandwidth limitation. The radio resources inwireless platforms are scarce and time-varying. Recentstudies 4, 6 reveal that the available bandwidth forindividual mobile users in 4G LTE networks generallyranges from 1.5to2.5 Mbps.
3) Path asymmetry. The different physical properties and time-varying network status result in the path asymmetry of heterogeneous access networks 4. The involvement of an unreliable communication path in multipath video transport only degrades the average quality.
4) TCP (MPTCP) 7 and Stream Control Transmission Protocol (SCTP) 8 are the transport-layer proto-cols recommended by IETF (Internet Engineering Tas Force) to enable parallel data transfer over multiple communication paths. However, both MPTCP and SCTP are ineffective for real-time video delivery since the packetretransmission mechanism incurs large end-to- end delay.

We consider how to utilize multiple wireless access net- works together for video streaming, e.g., using a combination of cellular, WiFi, and/or Bluetooth simultaneously. Here, as an example, Bluetooth and WiFi access networks are considered as we do not have end-to-end control over cellular links, and our work can be extended when other types of wireless access networks or more than two wireless access networks are used1. Since a wireless channel may suffer from time-varying fading, shadowing, interference and congestion, the available bandwidth of a wireless link may vary all the time. In addition, different smart phones or tablets may have different screen size and resolution. Taking these two aspects into consideration, the server should store several copies of video with different quality. The videos are encoded with SVC into a base layer and several enhancement layers, and chopped into segments and each segment can be played with a ?xed duration. We design a pull-based algorithm for video streaming, as shown in Fig. 1. After initialization, the client will request the video information which includes video resolutions, bit- rates and qualities from the server through both the WiFi and Bluetooth links. The rate adaptation agent will request a video segment of appropriate quality version based on the current queue length and estimated available bandwidth. Once the request decision is made, HTTP requests over both WiFi and Bluetooth will be issued to download the video segment. This process will continue until the completion of downloading the last segment or the termination of the video streaming by the user.

Fig 1.System Architecture
A.Real time search algorithm
This project proposed a Real time Adaptive algorithm to adapt the bit rate based on the band width condition of network for high quality with less buffering video streaming delivery 2. A real-time recursive best-action search algorithm, is use to obtain a sub-optimal solution for the future steps consideration to avoid long computation time which is shown in Algorithm 11. To meet the requirement of the real-time search, an important thing is to reduce the search duration for each state to an acceptable value. So for this we used small search depth D to invoke the search algorithm. For the current state s, all the possible actions A(s) will be enumerated. The recursive reward search algorithm is invoked to obtain the reward of state s with action a by enumerating all the possible future states S’ and their associated actions A'(s) 1. Real time adaptive algorithm is more stable and adaptable in dynamic situations, emphasizing the benefit of resource aggregation in multipath network scenarios 12. The project is basically work on video streaming process for downloading the video from the server this videos are stored on server with several format with different resolutions and this video is in the form of segments or called as frames. In wireless streaming applications, the characteristics of the source, the quality of the channel, and the occupancy of the playback buffer play major roles in the target video quality. Automatic repeat request (ARQ) and forward error correction (FEC) are commonly used to improve the reliability of the wireless link 15. To reduce the 3G traffic cost while maintain high video streaming quality, we have carefully devised the reward functions. By adjusting the parameters of the reward functions, we can make a trade-off between QoE metrics and the total cost. Through dynamic programming, we have obtained the optimal policy under the reward function settings 17.

Algorithm 1 Real-Time Best-Action Search Algorithm
1: Start procedure GETBESTACTION(s)
2: Initialize action??1, Qmax ?? ?
3: Generate all possible actions A(s) for state s
4: for all Action a ? A(s) do
q ? REWARDSEARCH(s, a, 0)
Check if q ; Qmax then
Qmax ? q, action ? a
end if and for loop.
5: return action
6: end procedure
7: Start procedure REWARDSEARCH(s, a, d)
8: q ? reward of (s, a)
9: if d ? D then
Return q and end if statement.
10: Generate all possible next states S’ of (s, a)
11: for all s’ from S’ do
12: Qmax ?? ?
13: Generate all possible actions A'(s) for state s’
14: for all Action a’? A’ (s) do
Qt ? REWARDSEARCH (s’, a’, d+ 1)
Check if Qt > Qmax then Qmax ? Qt
end if
end for
15: q ? q + ? Pss’ Qmax
16: end for
17: return q
18: end procedure
Adaptive Search Depth
The search depth can determine how good the search result is, and a larger value of depth will achieve a better result. Meanwhile, with the increment of the search depth, the search time to obtain the action for a segment will be increasedexponentially. Therefore, the search depth can be viewed as a trade-off between the video quality and the search time 1.

In this paper, a real-time adaptive best-action search algorithm for video streaming is use over wireless access networks for video streaming (downloading). To meet the requirement of the real-time search, an important issue is to reduce the search duration for each state to an acceptable value. First, we formulated the video streaming process as an MDP. To achieve smooth video streaming with high quality, the reward function is designed. Second, with the proposed rate adaptation algorithm, we can solve the MDP to obtain a sub-optimal solution in real time. Last, we explained the proposed algorithm. There are still some issues to investigate in the future. Like how to better allocate the loads between several links with finer granularity should be investigated.

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3 R. Mok, X. Luo, E. Chan, and R. Chang, “QDASH: a QoE-aware DASH system,” in ACM MMSys’12, 2012, pp. 11–22.
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8 S. Xiang, “Scalable Video Transmission over Wireless Networks,” Ph.D. dissertation, University of Victoria, 2013.
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