Cross Layer Optimization for Wireless Video Transmission Using Machine Learning

Basavarajaiah, Madhushree; Sharma, Priyanka

doi:10.1109/icrito.2018.8748435

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [42], the physical and media access control (MAC) layers work in conjunction with the application layer, matching the performance of the lower-layers data rate to that of the application layer bitrate using machine learning. For example, if the system is capable of using 64 quadrature amplitude modulation (QAM), but the demand dictates that 16-QAM is sufficient, then the system should opt for a less dense constellation with a lower probability of error.…”

Section: Video Streaming Solutionsmentioning

confidence: 99%

ASViS: A Protocol for Efficient Video Streaming Using SVC and Deadline-based Criteria.

Peña-Ancavil

Estevez

Orchard

et al. 2023

Preprint

View full text Add to dashboard Cite

Multimedia video streaming is the most used data consumption service on the Internet. Maintaining a data flow rate that provides the best video quality possible without overflowing the pipeline is challenging. Dynamic Adaptive Streaming over HTTP (DASH) is a commonly used protocol that sends video data in small packages called chunks. However, this technique can lead to video buffering issues and video pausing due to an empty video buffer. Adaptive Scalable Video Streaming (ASViS) is a more versatile protocol that uses scalable video coding, a flow-controlled user datagram protocol (UDP), and deadline-based criteria to manage the buffered data. Like transmission control protocol (TCP), ASViS adjusts the flow based on available bandwidth, and it can discard data based on video frame deadlines to prevent outdated data transmission. ASViS is compliant with RFC 8085, preventing it from overwhelming the Internet. The scalability feature allows ASViS to have the highest image quality possible in every frame allowed by SVC with the data layers available. A theoretical model is presented that predicts ASViS behavior under different network conditions. This work also shows how different parameters affect its performance and how it can be easily configured to optimize it.

show abstract

Section: Video Streaming Solutionsmentioning

confidence: 99%

ASViS: A Protocol for Efficient Video Streaming Using SVC and Deadline-based Criteria.

Peña-Ancavil

Estevez

Orchard

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [47], a collaboration between physical, Media Access Control (MAC), and application layers is observed. This alliance aligns the performance of the lower layer data rate with the application layer bitrate via machine learning.…”

Section: Modern Video Streaming Solutionsmentioning

confidence: 99%

Adaptive Scalable Video Streaming (ASViS): An Advanced ABR Transmission Protocol for Optimal Video Quality

Peña-Ancavil,

Estevez,

Sanhueza

et al. 2023

Electronics

View full text Add to dashboard Cite

Multimedia video streaming, identified as the dominant internet data consumption service, brings forth challenges in consistently delivering optimal video quality. Dynamic Adaptive Streaming over HTTP (DASH), while prevalent, often encounters buffering problems, causing video pauses due to empty video buffers. This study introduces the Adaptive Scalable Video Streaming (ASViS) protocol as a solution. ASViS incorporates scalable video coding, a flow-controlled User Datagram Protocol (UDP), and deadline-based criteria. A model is developed to predict the behavior of ASViS across varying network conditions. Additionally, the effects of diverse parameters on ASViS performance are evaluated. ASViS adjusts data flow similarly to the Transmission Control Protocol (TCP), based on bandwidth availability. Data are designed to be discarded by ASViS according to video frame deadlines, preventing outdated information transmission. Compliance with RFC 8085 ensures the internet is not overwhelmed. With its scalability feature, ASViS achieves the highest possible image quality per frame, aligning with Scalable Video Coding (SVC) and the available data layers. The introduction of ASViS offers a promising approach to address the challenges faced by DASH, potentially providing more consistent and higher-quality video streaming.

show abstract