An Effective and Innovative Streaming Model for Videos in Mobile Computing

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1 Asia-pacific Journal of Multimedia Services Convergence with Art, Humanities and Sociology Vol.3, No.1 (2013), pp An Effective and Innovative Streaming Model for Videos in Mobile Computing Edward E. Pingoy 1), Osvaldo Gervasi 2) Abstract Video streaming in mobiles is based on the stream of traffic over mobile networks. The capacity of wireless link cannot be provided on demand. The poor service quality of video streaming over mobile networks is based on the gap between the link capacity and the traffic demand, along with time-varying link conditions, such as long buffering time and intermittent disruptions. In this paper we propose to develop a new mobile video framework, labeled as AMES-Cloud, which is divided into two main parts: AMoV (adaptive mobile video streaming) and EsoV (Efficient social video sharing). AMoV and EsoV build up the third party agent to provide efficient video streaming services for every mobile user. The AMoV and WSoV will provide the different types of features like, based on the feedback of link quality the AMoV contracts the private agent adaptively adjust her streaming flow with a scalable video coding technique. The second feature EsoV monitors the social network interactions among mobile users, and their private agents try to prefect video content in advance. In this paper we implement a prototype of the AMES-Cloud framework to show its efficiency. For that we propose to show that the third party agents in the cloud can effectively provide the adaptive streaming and perform video sharing. Keywords : Scalable Video Coding, Adaptive Video Streaming, Mobile Networks, Social Video Sharing, Cloud Computing. 1. Introduction Now a day s mobile device allow users to run any type of powerful applications. Those will take advantage of sharing the data from one place to other place and have the better exchange capabilities of mobile devices. As a result, mobile applications faultlessly incorporate with real time data streams and Web 2.0 applications, such as mashups, open collaboration, social networking and mobile commerce [1], [2]. The mobile execution platform is being used to playing games; capturing, editing, annotating and uploading video; handling finances; managing personal health, micro payments, ticket purchase, interacting with ubiquitous computing infrastructures. Even mobile device hardware and mobile networks continue to develop and to improve, mobile devices will Received(April 20, 2013), Review request(april 21, 2013), Review Result(1st: May 16, 2013) Accepted(June 30, 2013) 1 Saint Anthony's College, Philippines epingoy@gmail.com 2 (Corresponding Author) Dept. of Mathematics and Computer Science, University of Perugia, Italy. osvaldo@unipg.it ISSN: AJMSCAHS Copyright c 2013 SERSC 25

2 An Effective and Innovative Streaming Model for Videos in Mobile Computing always be resource-poor, less secure, with unstable connectivity, and with less energy since they are powered by battery [3]. In this paper we are proposed to show, how the adaptive video streaming is done by using the AMoV (adaptive mobile video streaming) and ESoV (efficient social video sharing). While growing the more traffic is suspected by video streaming and downloading in mobiles third party services are provided over the past few years [4]. In mobile networks video streaming is not challenging, the problem with only mobile networks. Mobile networks have been suffering from video traffic transmissions over limited bandwidth of wireless links. So many mobile operators enhance the wireless link bandwidth (e.g., 3G and LTE); soaring video traffic demands from mobile users are rapidly over whelming the wireless link capacity. In mobile video streaming via 3G/4G mobile networks, mobile users often suffer long buffering time and intermittent disruptions due to the limited bandwidth and link condition fluctuation caused by multi-pat fading and user mobility [5] [6] [7]. Thus, it is crucial to improve the service quality of mobile video streaming while using the networking and computing resources efficiently [8] [9] [10] [11]. SCALABILITY: Mobile video streaming services should support a wide range of mobile devices; then have different features like video resolutions, different wireless links (like 3G and LTE) and etc. The capacity of link for a mobile device may vary over time and space depending on its signal strength, other user s traffic in the same cell, and link condition variation. Storing multiple versions (with different bit rates) of the same video content may incur high overhead in terms of storage and communication. To address this issue, the Scalable Video Coding (SVC) technique (Annex G extension) of the H.264 AVC video compression standard [9] [10] [11] defines a base layer (BL) with multiple enhance layers (ELs). ADAPTABILITY: The relatively stable traffic links between servers and users perform poorly in mobile environments. Scalable video coding and adaptive streaming techniques can be jointly combined to accomplish effectively the best possible quality of video streaming services. 2. Related Work D. Kovachev stated that With the advent of mobile smart phones, new types of virtual communities arrived, i.e. mobile communities. Mobile communities, with respect to their IT needs, introduce new requirements compared to traditional online web communities. On the other hand, cloud computing is emerging as computing concept that promises provision of computational resources on demand and abstraction of technical details from the clients. We propose Mobile Community Cloud Platform (MCCP) as a cloud computing system that can leverage the full potential of mobile community growth. An analysis of the core requirements of common mobile communities is provided before we present the design of our cloud computing architecture that supports building and evolving of mobile communities. 26 Copyright c 2013 SERSC

3 Asia-pacific Journal of Multimedia Services Convergence with Art, Humanities and Sociology Vol.3, No.1 (2013) M. Satyanarayanan stated that mobile computing continuously evolves through the sustained effort of many researchers. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple user s interactively generating and receiving media such as high-definition video and high-resolution images. Y. Li, Y. Zhang stated that large scale, Internet based mobile TV deployment presents both tremendous opportunities and challenges for mobile operators and technology providers. This paper presents a measurement based study on a large scale mobile TV service offering in China. Within the one month measurement period, our dataset captured over 1 million unique mobile devices and more than 49 million video sessions. Analysis showed that mobile viewing patterns are different from that of landline based IPTV and VoD systems. In particular, the average viewing time is significantly shorter, and the channel popularity distribution is more skewed towards top ranked channels than that of landline based systems. For the channel sojourn time, the distribution follows a piecewise model, which combines lognormal and pareto distribution. The lognormal part, which fits the majority of video sessions, more closely resembles the mobile phone call holding time, rather than the power law distribution in the landline IPTV case. In comparing the 3G and WiFi access methods, we found that users exhibit different behaviors when accessing from different networks. In 3G networks, where users are subject to data charge, users tend to have shorter channel sojourn time and prefer lower bit-rate channels. The parameters of the distributions are also different. Understanding these user behaviors and their implications on network traffic are critical for the success of future mobile TV industry. 3. Existing System In this paper, we design a adaptive video streaming and prefetching framework for mobile users with the above objectives in mind, dubbed AMES-Cloud. AMES-Cloud constructs a third party agent for each mobile user in cloud computing environments. All these are used in 1) AMoV (adaptive mobile video streaming) 2) ESoV (Effective social video sharing) ISSN: AJMSCAHS Copyright c 2013 SERSC 27

4 An Effective and Innovative Streaming Model for Videos in Mobile Computing AMoV (adaptive mobile video streaming): AMoV is the most featured i,e having streaming experiences by adaptively control the streaming bitrate based on the link quality. It adjusts the bit rate for every user in the scalable video coding. Every time link updates will be keep track by the third part agent. Here the real time SVC coding is done on the cloud computing side efficiently. ESoV (Effective social video sharing): ESoV seeks to provide a user with instant playing of video clips by prefetching the video clips in advance from her private agent to the local storage of her device. The strength of the social links between users and the history of various social activities can probabilistically determine how much and which video will be perfected. 4. Proposed System 4.1 AMES-CLOUD FRAMEWORK In this section we are explaining the two parts AMoV and ESoV which belongs to AMES-Cloud framework. Figure: 1 explains the complete video storing and streaming system in cloud is called the Video Cloud (VC).It is very large scale base (VB), which will store the most popular and important video clips for the video service providers (VSPs).For the popular videos, a temporal video base (tempvb) is used to cache new candidates, here tempvb counts the access frequency of each video. [Fig: 1] AEMS Cloud Framework Note that as the cloud service may across different places, or even continents, so in the case of a video delivery and prefetching between different data centers, an transmission will be carried out, which can be then called copy. And because of the optimal deployment of data centers, as well as the capable links among the data centers, the copy of a large video file takes tiny delay [12]. The two parts, AMoV and EMoS, in AMES-Cloud framework have tight connections and will together 28 Copyright c 2013 SERSC

5 Asia-pacific Journal of Multimedia Services Convergence with Art, Humanities and Sociology Vol.3, No.1 (2013) service the video streaming and sharing: they both rely on the cloud computing platform and are carried out by the private agencies of users; while prefetching in EMoS, the AMoV will still monitor and improve the transmission considering the link status; with a certain amount of perfected segments by EMoS, AMoV can offer better video quality. With the efforts of AMoV and EMoS, we illustrate the flow chart of how a video will be streamed in Fig. 5. Note that in order to exchange the videos among the localvbs, subvbs, tempvb and the VB, a video map (VMap) is used to indicate the required segment. Once a mobile user starts to watch a video by a link, the local will first be checked whether there are any perfected segments of the video so that it can directly start. If there is none or just some parts, the client will report a corresponding VMap to its subvc. If the subvc has perfected parts in subvb, the subvc will initiate the segment transmission. But if there is also none in the subvb, the tempvb and VB in the center VC will be checked. For a non-existing video in AMES-Cloud, the collector in VC will immediately fetch it from external video providers via the link; after re-encoding the video into SVC format, taking a bit longer delay, the subvc will transfer to the mobile user. Also in AMES-Cloud, if a video is shared among the subvcs at a certain frequency threshold (e.g., 10 times per day), it will be uploaded to the tempvb of the VC; and if it is further shared at a much higher frequency (e.g., 100 times per day), it will be stored with a longer lifetime in the VB. In such a manner, which is quite similar to the leveled CPU cache, the subvb and VB can always store fresh and popular videos in order to increase the probability of re-usage. 5. Experimental Results We evaluate the performance of the AMES-Cloud framework by a prototype implementation. We choose the U-cloud server (premium) in the cloud computing service offered by Korean Telecom, and utilize the virtual server with 6 virtual CPU cores (2.66GHz) and 32GB memory, which is fast enough for encoding 481P (481 by 721) video with H.265 SVC format in 31 fps at real time [11]. In the cloud, we deploy our server application based on Java, including one main program handling all tasks of the whole VC, while the program dynamically initializes, maintains and terminates instances of another small Java application as private agents for all active users. We implement the mobile client at a mobile phone, Samsung Galaxy II, with android system version 4.0. The mobile data service is offered by LG U+ LTE network, while in some uncovered area the 3G network is used. Note that we still use 3G to indicate the general cellular network. We test in the downtown area, so the practical bandwidth of the mobile link is not as high as we expected, but this won t impact our experiment results. The test video is the Tomb Raider 2012 Trailer in H.265 format with 481P resolution downloaded from YouTube. Its size is Mbytes and with a duration of 180 seconds. We first decode it by the x264 decoder into the YUV format, and re-encode it by the H.265 SVC encoder, the Joint Scalable ISSN: AJMSCAHS Copyright c 2013 SERSC 29

6 An Effective and Innovative Streaming Model for Videos in Mobile Computing Video Model (JSVM) software of version 9.1 [40]. We just use default settings for the decoding and encoding, and do the H.265 SVC encoding at the virtual server in the cloud. We split the video into segments by 1 second to 5 seconds that is to vary Twin with values 1s, 2s, 3s, 4s and 5s. By JSVM, besides the base layer, we further make five temporal layers (1.875, 3.75, 7.5, 15, and 16 fps), two spatial layers (241 by 361 and 121 by 181) and two more quality layer (low and high), referring to [14] and [12]. Thus we define the best resolution configuration as And we also test different resolution configurations, including , , and Conclusion In this paper, we discussed our proposal of an adaptive mobile video streaming and sharing framework, called AMES-Cloud, which efficiently stores videos in the clouds (VC), and utilizes cloud computing to construct private agent (subvc) for each mobile user to try to offer non-terminating video streaming adapting to the fluctuation of quality of link on the scalable video coding technique. AMES-Cloud also providing "non-buffering" experience of the video streaming by background functions among the VB, subvbs and localvb of mobile users. Results show that the AMES-Cloud brings the vast improvement on the adaptively of the mobile streaming. In this paper, verifying how the cloud computing can improve the transmission adaptability and prefetching for mobile users. In future, we will also try to improve the SNS-based prefetching, and security issues in the AMES-Cloud. 30 Copyright c 2013 SERSC

7 Asia-pacific Journal of Multimedia Services Convergence with Art, Humanities and Sociology Vol.3, No.1 (2013) References [1] A. Wright, Get Smart, Communications of the ACM, vol. 52, no. 1, (2009), pp [2] D. Kovachev, D. Renzel, R. Klamma and Y. Cao, Mobile Community Cloud Computing: Emerges and Evolves, in Proceedings of the First International Workshop on Mobile Cloud Computing (MCC), Kansas City, MO, USA: IEEE, (2010). [3] M. Satyanarayanan, P. Bahl, R. Caceres and N. Davies, The Case for VM-Based Cloudlets in Mobile Computing, IEEE Pervasive Computing, vol. 8, no. 4, (2009) October, pp [4] CISCO, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, , Tech. Rep., (2012). [5] Y. Li, Y. Zhang and R. Yuan, Measurement and Analysis of a Large Scale Commercial Mobile Internet TV System, in ACM IMC, (2011), pp [6] T. Taleb and K. Hashimoto, MS2: A Novel Multi-Source Mobile-Streaming Architecture, in IEEE Transaction on Broadcasting, vol. 57, no. 3, (2011), pp [7] X. Wang, S. Kim, T. Kwon, H. Kim and Y. Choi, Unveiling the BitTorrent Performance in Mobile WiMAX Networks, in Passive and Active Measurement Conference, (2011). [8] A. Nafaa, T. Taleb and L. Murphy, Forward Error Correction Adaptation Strategies for Media Streaming over Wireless Networks, In IEEE Communications Magazine, vol. 46, no. 1, (2008), pp [9] J. Fernandez, T. Taleb, M. Guizani and N. Kato, Bandwidth Aggregation-aware Dynamic QoS Negotiation for Real-Time Video Applications in Next-Generation Wireless Networks, in IEEE Transaction on Multimedia, vol. 11, no. 6, (2009), pp [10] T. Taleb, K. Kashibuchi, A. Leonardi, S. Palazzo, K. Hashimoto, N. Kato and Y. Nemoto, A Cross-layer Approach for An Efficient Delivery of TCP/RTP-based Multimedia Applications in Heterogeneous Wireless Networks, in IEEE Transaction on Vehicular Technology, vol. 57, no. 6, (2008), pp [11] K. Zhang, J. Kong, M. Qiu and G.L Song, Multimedia Layout Adaptation Through Grammatical Specifi cations, in ACM/Springer Multimedia Systems, vol. 10, no. 3, (2005), pp [12] G. Wang and T. E. Ng, The Impact of Virtualization on Network Performance of Amazon EC2 Data Center, in IEEE INFOCOM, (2010). [13] JSVM, [14] P. McDonagh, C. Vallati, A. Pande and P. Mohapatra, Quality-Oriented Scalable Video Delivery Using H. 264 SVC on An LTE Network, in WPMC, (2011). ISSN: AJMSCAHS Copyright c 2013 SERSC 31

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