Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/224134
Title: Dynamic Adaptive Video Streaming on Heterogeneous TVWS and Wi-Fi Networks
Authors: Luca Bedogni;Angelo Trotta;Marco Di Felice;Yue Gao;Xingjian Zhang;Qianyun Zhang;Fabio Malabocchia;Luciano Bononi
Year: 2017
Publisher: IEEE
Abstract: Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion&x2019;s share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15&x0025; of average quality index) in scenarios in which the Wi-Fi is received at a low strength.
URI: http://localhost/handle/Hannan/224134
volume: 25
issue: 6
More Information: 3253,
3266
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8008810.pdf4.71 MBAdobe PDF
Title: Dynamic Adaptive Video Streaming on Heterogeneous TVWS and Wi-Fi Networks
Authors: Luca Bedogni;Angelo Trotta;Marco Di Felice;Yue Gao;Xingjian Zhang;Qianyun Zhang;Fabio Malabocchia;Luciano Bononi
Year: 2017
Publisher: IEEE
Abstract: Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion&x2019;s share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15&x0025; of average quality index) in scenarios in which the Wi-Fi is received at a low strength.
URI: http://localhost/handle/Hannan/224134
volume: 25
issue: 6
More Information: 3253,
3266
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8008810.pdf4.71 MBAdobe PDF
Title: Dynamic Adaptive Video Streaming on Heterogeneous TVWS and Wi-Fi Networks
Authors: Luca Bedogni;Angelo Trotta;Marco Di Felice;Yue Gao;Xingjian Zhang;Qianyun Zhang;Fabio Malabocchia;Luciano Bononi
Year: 2017
Publisher: IEEE
Abstract: Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion&x2019;s share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15&x0025; of average quality index) in scenarios in which the Wi-Fi is received at a low strength.
URI: http://localhost/handle/Hannan/224134
volume: 25
issue: 6
More Information: 3253,
3266
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8008810.pdf4.71 MBAdobe PDF