RSLCD—5G RAN Slicing-enabled Low Complexity Adaptive Streaming Solution for Live CMAF and DASH Content Delivery

5G network slicing marks a major paradigm shift in mobile networks by utilizing physical infrastructure across various technological domains to support a wide range of unique and high-demand applications (such as 2D/360° video, AR, VR, etc.) with differing Quality of Service (QoS) requirements. Moreover, the 3GPP 5G New Radio (NR) specification introduces promising features like Bandwidth Parts (BWPs) and mixed numerologies, enabling more flexibility in resource allocation within a given carrier and in the configuration of waveform parameters. These features can be leveraged in network slicing to meet the diverse needs of 5G services, particularly in the delivery of on-demand and live video. However, as network slicing continues to mature, the effective implementation and management of Radio Access Network (RAN) slices for these multifaceted services remains a considerable challenge. This paper presents an innovative 5G RAN slicing-enabled adaptive streaming solution for live CMAF (Common Media Application Format (ISO/IEC 23000-19)) and DASH (Dynamic Adaptive Streaming Over HTTP) content delivery, named RSLCD. In contrast to existing state-of-the-art live streaming solutions, RSLCD does not require hard-coded parameters or underlying application or network model assumptions. The low-complexity RSLCD solution considers useful playback states during CMAF and DASH streaming profiles and involves comprehensive exploration and exploitation of playback parameters such as bitrate proximity, playback smoothness, download factor, and buffer levels to optimally decide the upcoming bitrates in a 5G RAN slicing environment. The comprehensive NS3 streaming results validate the superior performance of the proposed solution, demonstrating noticeable improvements in visual quality, quality smoothness, and achieved throughput compared to state-of-the-art solutions such as L2A, Festive, LLAMA, MPC, and Stallion.