Low overhead spatiotemporal video compression over smartphone based Delay Tolerant Network

Pal, Tamal; Bit, Sipra Das

doi:10.1016/j.jvcir.2020.102813

Cited by 6 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the first part considers transmission time and energy consumption as the evaluation metrics where references [21,22] are used as the baselines, shorten for IoTJ and CoMNeT, respectively. e second part considers frame loss rate and transmission time as the evaluation metrics where references [30,31] are used as the baselines, shorten for CoMCoM and JVCIR, respectively. e third part considers response time and QoE of users as the evaluation metrics by collecting the 1000 football competitions and testing 500 users.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…ey measured the quality of the transmitted video and available bandwidth with the proposed technique and also built a quality multimedia content and transferred over the transmitter. In [31], the authors provided a lightweight video compression scheme through interframe and intraframe compression. In interframe compression, redundant frames were removed by a proposed interpolation search-based method and a lightweight edge detection technique.…”

Section: Video Compressionmentioning

confidence: 99%

“…Regarding the video transmission, the video compression algorithm is very important, which can accelerate the video transmission and guarantee users to obtain the contents of football competitions as quickly as possible. However, the current video compression algorithms exit some limitations [27][28][29][30][31], and especially, the frame loss rate and the transmission time cannot reach the satisfactory level.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In-Network Caching and Edge Computing-Based Live Broadcasting Optimization for Football Competitions

2020

Mobile Information Systems

View full text Add to dashboard Cite

The business of football competitions is called the number one sport in the world, thanks to more than one billion people’s attention. With the development of big convergence media, the live broadcasting of football competitions gradually becomes industrialization and commercialization, which has a direct relationship with economic growth. For the live broadcasting of football competitions, the users focus more on quality of experience, i.e., definition and instantaneity. In terms of such two metrics, the current live broadcasting schemes are difficult to cover them well. Therefore, this paper exploits the emerging in-network caching and edge computing technologies to optimize the live broadcasting of football competitions, shorten for IELB. At first, the live broadcasting optimization framework based on in-network caching and edge computing is presented. Then, the auction-based method is used to address the task scheduling problem in the edge computing. In addition, a video compression algorithm based on adaptive convolution kernel is introduced to accelerate the video transmission and guarantee users to obtain the contents of football competitions as quickly as possible. The proposed IELB has been verified based on the collected real football competitions dataset by evaluating response time, and the experimental results demonstrate that IELB is feasible and efficient.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Section: Video Compressionmentioning

confidence: 99%