Cost-Effective, Quality-Oriented Transcoding of Live-Streamed Video on Edge-Servers

Live-streaming video requires a lot of CPU-intensive transcoding so that viewers can receive video at bitrates appropriate to their devices and network conditions, which is necessary for a good quality of experience (QoE). We allocate transcoding tasks to edge-servers in a multiple-access edge-computing (MEC) architecture, taking into account server capacity, wireless network coverage, and the cost budget of broadcasters, as well as QoE. Our algorithm first chooses candidate transcoding tasks by giving higher priority to the tasks that make the most cost-effective contribution to popularity-weighted video quality (PWQ). It assigns these tasks to edge-servers in a greedy manner, taking network coverage and computational load into account. Subsequently, it meets a cost budget by reassigning some tasks and removing other assignments altogether, while trying to minimize the effect of these alterations on total PWQ. Simulation results show that our scheme achieves 0.06% to 94.62% (average 25.3%) more PWQ than alternative schemes under the same cost budget.