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PROJECT TITLE :

Enabling Adaptive High-Frame-Rate Video Streaming in Mobile Cloud Gaming Applications

ABSTRACT:

High-frame-rate (HFR) video is emerging in widespread gaming applications to boost the graceful experience perceived by end users. However, it's difficult to ensure the delivery quality of HFR video in mobile cloud gaming eventualities as a result of of the high transmission rate and restricted wireless resources. To deal with this vital downside, we have a tendency to develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this theme’s capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. Initial, we tend to propose an online video frame choice algorithm to reduce the total distortion primarily based on the network status, input video knowledge, and delay constraint. Second, we introduce an unequal FEC coding scheme to produce differentiated protection for Intra (I) and Predicted (P) frames with low-latency price. The proposed APHIS framework is able to appropriately filter video frames and alter data protection levels to optimize the quality of HFR video streaming. We tend to conduct intensive emulations in Exata involving HFR video encoded with H.264 codec. Experimental results show that APHIS outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, finish-to-finish delay, and goodput. Thus, we have a tendency to advocate APHIS for delivering HFR video streaming in mobile cloud gaming systems.


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