Adaptive video streaming in mobile cloud computing

Tamizhselvi, S.P.; Muthuswamy, Vijayalakshmi

doi:10.1109/iccic.2014.7238513

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Moreover, in [26], Staelens et al conducted novel experiments on tablet devices in more ecologically valid testing environments using longer duration video sequences. Furthermore, in [30], Tamizhselvi et al proposed a system which provides continuous streaming of videos according to the status of the mobile device and latency of the network. In [31], Carlsson et al proposed a framework which is the first to perform quality adaptation of a set of conventional video-on-demands streams based on both current bandwidth conditions and switching probabilities.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Objective Video Crowdsourcing Method in Mobile Environment

et al. 2019

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With the rapid development of mobile video services, HD and UHD videos are attractive for mobile users due to the realistic visual enjoyment and the accurate representation. However, the limited transmission bit rate in 4G communication network affects the experience of the users for watching videos. Crowdsourcing is considered as a reasonable and effective solution to alleviate the resource limitation. Through employing the crowdsourcing participants to download and transmit video segments, mobile users can get enhanced video services. However, it is still a significant challenge that how to avoid excessive payment and energy consumption when the crowdsourcing participants download the video segments for the mobile users. To address this challenge, a multi-objective video crowdsourcing method in mobile environment is proposed in this paper. Technically, the crowdsourcing participants apply device-to-device (D2D) communication technique rather than the cellular network or bluetooth transmission to transmit video segments to the mobile users. Here, we divide our problem into two situations, the single participant case and the multi-participants case. In the single participant case, we apply the improved dynamic programming algorithm to find strategies with more enhanced video service time that the crowdsourcing participants provide for the mobile users. Then Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multiple Criteria Decision Making (MCDM) techniques are applied to find a balanced strategy to maximize the enhanced video service time and minimize the payment and the energy consumption. In the multi-participants case, through DBSCAN clustering, the problem with multi-participants is divided into several problems with single participant. Finally, extensive experimental evaluations are conducted to demonstrate the effectiveness and efficiency of our proposed method.

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Section: Related Workmentioning

confidence: 99%

A Multi-Objective Video Crowdsourcing Method in Mobile Environment

et al. 2019

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“…Some of the multimedia challenges faced by a streaming video are delay and network performance. [6]. Apart from these QoS(Quality of Service) challenges, smartphones need to solve network QoS issues such as a battery, energy consumption, low bandwidth, latency, and packet loss.…”

Section: Introductionmentioning

confidence: 99%

“…Whenever, a device state change occurs, the device information updates in the cloud server for the registered mobile users and the estimated bandwidth fits the congestion window. There is a lot of research on bandwidth estimation [14], and TCP congestion control algorithms exist in the literature [15][16][17][18][19][20]. Lai et al shows that the buffering in the transport layer reduced to deliver a good quality of video live streaming in mobile [13].…”

Section: Introductionmentioning

confidence: 99%

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

Tamizhselvi

Muthuswamy

2021

Peer-to-Peer Netw. Appl.

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In recent years, smartphone users are interested in large volumes to view live videos and sharing video resources over social media (e.g., Youtube, Netflix). The continuous streaming of video in mobile devices faces many challenges in network parameters namely bandwidth estimation, congestion window, throughput, delay, and transcoding is a challenging and time-consuming task. To perform these resource-intensive tasks via mobile is complicated, and hence, the cloud is integrated with smartphones to provide Mobile Cloud Computing (MCC). To resolve the issue, we propose a novel framework called delay aware bandwidth estimation and intelligent video transcoder in mobile cloud. In this paper, we introduced four techniques, namely, Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS). To evaluate the performance of the proposed algorithm, we implemented a testbed using the two mobile configurations and the public cloud server Amazon Web Server (AWS). The study and results in a real environment demonstrate that our proposed framework can improve the QoS requirements and outperforms the existing algorithms. Firstly, MMBCE utilizes the well-known Markov Decision Process (MDP) model to estimate the best bandwidth of mobile using reward function. MMBCE improves the performance of 50% PDR compared with other algorithms. CDCW fits the congestion window and reduces packet loss dynamically. CDCW produces 40% more goodput with minimal PLR. Next, in QVPS, the M/M/S queueing model is processed to reduce the video processing delay and calculates the total service time. Finally, IVTS applies the M/G/N model and reduces 6% utilization of transcoding workload, by intelligently selecting the minimum workload of the transcoding server. The IVTS takes less time in slow and fast mode. The performance analysis and experimental evaluation show that the queueing model reduces the delay by 0.2 ms and the server’s utilization by 20%. Hence, in this work, the cloud minimizes delay effectively to deliver a good quality of video streaming on mobile.

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