Modeling and resource allocation for mobile video over WiMAX broadband wireless networks

Al-Tamimi, Abdel-Karim; So-In, Chakchai; Jain, Raj

doi:10.1109/jsac.2010.100407

Cited by 44 publications

(19 citation statements)

References 22 publications

(22 reference statements)

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“…The state transition chain of a leaky bucket is shown in Fig. 3 [26][27][28], where each state of the chain indicates the number of packets inside the bucket. Since the size of the bucket is w, any probability state i≤ w is the in-service state and the states i> w are the states in queue.…”

Section: Traffic Shaping Schemementioning

confidence: 99%

Performance Evaluation of the WiMAX Network under a Complete Partitioned User Group with a Traffic Shaping Algorithm

Akhter¹,

Islam²,

Amin³

2014

Journal of Information Processing Systems

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Abstract-To enhance the utilization of the traffic channels of a network (instead of allocating radio channel to an individual user), a channel or a group of channels are allocated to a user group. The idea behind this is the statistical distribution of traffic arrival rates and the service time for an individual user or a group of users. In this paper, we derive the blocking probability and throughput of a subscriber station of Worldwide Interoperability for Microwave Access (WiMAX) by considering both the connection level and packet-level traffic under a complete partition scheme. The main contribution of the paper is to incorporate the traffic shaping scheme onto the incoming turbulent traffic. Hence, we have also analyzed the impact of the drain rate of the buffer on the blocking probability and throughput.

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Section: Traffic Shaping Schemementioning

confidence: 99%

Performance Evaluation of the WiMAX Network under a Complete Partitioned User Group with a Traffic Shaping Algorithm

Akhter¹,

Islam²,

Amin³

2014

Journal of Information Processing Systems

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show abstract

“…These attributes are important to achieve the desired results and to allow the analysis of our large collection of video traces. Our pre-analysis step resulted in choosing three modeling methods: autoregressive (AR) model, autoregressive integrated moving average (ARIMA) model using the approach proposed in [13], and SAM model [14,15]. All these models use maximum likelihood estimation to determine the model terms coefficients and consider Akaike's Information Criterion (AIC) as their optimization goal.…”

Section: Modeling Hd Video Tracesmentioning

confidence: 99%

“…We present results based on over 50 HD video traces. We compare three modeling methods: autoregressive (AR) [12], autoregressive integrated moving average (ARIMA) [12] using the approach proposed in [13], and our Simplified Seasonal ARIMA Model (SAM) that was developed for the less resource demanding mobile video traces [14,15]. In addition we compare these models in their prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

High-Definition Video Streams Analysis, Modeling, and Prediction

Al-Tamimi

Jain

So-In

2012

Advances in Multimedia

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High-definition video streams' unique statistical characteristics and their high bandwidth requirements are considered to be a challenge in both network scheduling and resource allocation fields. In this paper, we introduce an innovative way to model and predict high-definition (HD) video traces encoded with H.264/AVC encoding standard. Our results are based on our compilation of over 50 HD video traces. We show that our model, simplified seasonal ARIMA (SAM), provides an accurate representation for HD videos, and it provides significant improvements in prediction accuracy. Such accuracy is vital to provide better dynamic resource allocation for video traffic. In addition, we provide a statistical analysis of HD videos, including both factor and cluster analysis to support a better understanding of video stream workload characteristics and their impact on network traffic. We discuss our methodology to collect and encode our collection of HD video traces. Our video collection, results, and tools are available for the research community.

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“…In previous research results, it was shown that SAM is capable of capturing the statistical characteristics of video traces with less than 1% difference from the optimal models for each video trace. The model has been tested against video traces encoded using different encoding settings and standards: MPEG-Part2, MPEG4-Part10/AVC, and AVC's scalable extension for temporal scalability (SVC-TS) [9,10]. SAM can be represented using the simplified notation: (8) where z here represents the seasonality of the video trace.…”

Section: Amentioning

confidence: 99%

“…Simplified Seasonal autoregressive integrated moving average (ARIMA) model, or SAM, has demonstrated its capability of modeling movies encoded with different encoding settings and standards [9,10]. In addition, our simplification of SARIMA modeling as represented in SAM, as shown in Section II, allows real time implementation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic resource allocation based on online traffic prediction for video streams

Al-Tamimi

Jain

So-In

2010

2010 IEEE 4th International Conference on Internet Multimedia Services Architecture and Application

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Abstract-In this paper, we propose a new dynamic resource allocation (DRA) scheme to support the constantly increasing online video stream traffic, especially high definition (HD) video streams. Our DRA scheme is based on online traffic prediction using seasonal time analysis. Our scheme seeks to provide accurate traffic prediction, to minimize the resource negotiation frequency, and to increase the utilization of the network resources while meeting maximum delay requirements. We validate our approach using various video traces, including our video collection of more than 50 HD video traces. We show through our results that our proposed scheme achieve up to 19.8% improvement in allocating bandwidth for short-length video traces, and up to 25% for long traces compared to the variable step-size adaptive (VSA) algorithm.

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Modeling and resource allocation for mobile video over WiMAX broadband wireless networks

Cited by 44 publications

References 22 publications

Performance Evaluation of the WiMAX Network under a Complete Partitioned User Group with a Traffic Shaping Algorithm

Performance Evaluation of the WiMAX Network under a Complete Partitioned User Group with a Traffic Shaping Algorithm

High-Definition Video Streams Analysis, Modeling, and Prediction

Dynamic resource allocation based on online traffic prediction for video streams

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