Enabling Adaptive High-Frame-Rate Video Streaming in Mobile Cloud Gaming Applications
High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resour...
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| Veröffentlicht in: | IEEE transactions on circuits and systems for video technology 2015-12, Vol.25 (12), p.1988-2001 |
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| container_end_page | 2001 |
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| container_issue | 12 |
| container_start_page | 1988 |
| container_title | IEEE transactions on circuits and systems for video technology |
| container_volume | 25 |
| creator | Wu, Jiyan Yuen, Chau Cheung, Ngai-Man Chen, Junliang Chen, Chang Wen |
| description | High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme's capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive 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, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems. |
| doi_str_mv | 10.1109/TCSVT.2015.2441412 |
| format | Article |
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However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme's capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive 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, end-to-end delay, and goodput. 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However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme's capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive 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, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems.</description><subject>Bandwidth</subject><subject>Cloud computing</subject><subject>Distortion</subject><subject>Forward error correction</subject><subject>High frame rate video</subject><subject>mobile cloud gaming</subject><subject>Packet loss</subject><subject>Streaming media</subject><subject>unequal error protection</subject><subject>Video coding</subject><subject>video frame selection</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwA7CJxDrFduzYXlZRH0hFSPSxtRxnUlylcXBSJP6elFSsZjS6d-bOQeiR4AkhWL1ssvVuM6GY8AlljDBCr9CIcC5jSjG_7nvMSSwp4bform0PGBMmmRih7aw2eeXqfTQtTNO5b4iWbv8Zz4M5QvxhOoh2rgAfrbsA5ngWujp687mrIMoqfyqixTCeNk3lrOmcr9t7dFOaqoWHSx2j7Xy2yZbx6n3xmk1XsaWKdzFLgUueS2GESDG3KhfQZzeWFkaVZc5EqhQWRWGppVCCFJgKAworm1OZ5MkYPQ97m-C_TtB2-uBPoe5PaiIYk0naf9-r6KCywbdtgFI3wR1N-NEE6zM-_YdPn_HpC77e9DSYHAD8GwQhkjCW_AKYPWtB</recordid><startdate>201512</startdate><enddate>201512</enddate><creator>Wu, Jiyan</creator><creator>Yuen, Chau</creator><creator>Cheung, Ngai-Man</creator><creator>Chen, Junliang</creator><creator>Chen, Chang Wen</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme's capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive 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, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSVT.2015.2441412</doi><tpages>14</tpages></addata></record> |
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| identifier | ISSN: 1051-8215 |
| ispartof | IEEE transactions on circuits and systems for video technology, 2015-12, Vol.25 (12), p.1988-2001 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Bandwidth Cloud computing Distortion Forward error correction High frame rate video mobile cloud gaming Packet loss Streaming media unequal error protection Video coding video frame selection |
| title | Enabling Adaptive High-Frame-Rate Video Streaming in Mobile Cloud Gaming Applications |
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