Joint Transmission and Transcoding in Computing Power Networks for Livecast: A Quantum-inspired Optimization Approach.

With the continuous development of network technology and hardware devices, panoramic live cast is promising and widely used in various industries. To meet massive heterogeneous viewer demands, live cast video streams must be transcoded into multiple versions and then transmitted to viewers. By offloading transcoding workloads to network nodes closer to broadcasters and viewers, computing power networks (CPN) have been considered an effective means to provide viewers with a higher quality of experience (QoE). In this paper, we study the joint video transmission and transcoding resource allocation in the CPN-based panoramic livecast system. Considering the versatility and simplicity, we offer a multi-layer network model to capture the stochastic characteristics of transmission and transcoding processes and transform the joint resource allocation problem into a broader shortest path problem (SPP). As the scale of networks expands, the SPP may become intractable on classical computers. This paper explores the viability of solving the SPP on quantum computers by utilizing quantum resources such as superposition and entanglement, then proposes a shortest path algorithm based on the quantum approximate optimization algorithm (QAOA-SPP) for jointly optimizing the latency and overhead of transmission and transcoding. Simulation results indicate that our algorithm can achieve better performance than the benchmark schemes under reasonable parameter settings.