  The evolution of 5G and Beyond networks has enabled new applications with
stringent end-to-end latency requirements, but providing reliable low-latency
service with high throughput over public wireless networks is still a
significant challenge. One of the possible ways to solve this is to exploit
path diversity, encoding the information flow over multiple streams across
parallel links. The challenge presented by this approach is the design of joint
coding and scheduling algorithms that adapt to the state of links to take full
advantage of path diversity. In this paper, we address this problem for a
synchronous traffic source that generates data blocks at regular time intervals
(e.g., a video with constant frame rate) and needs to deliver each block within
a predetermined deadline. We first develop a closed-form performance analysis
in the simple case of two parallel servers without any buffering and
single-packet blocks, and propose a model for the general problem based on a
Markov Decision Process (MDP). We apply policy iteration to obtain the coding
and scheduling policy that maximizes the fraction of source blocks delivered
within the deadline: our simulations show the drawbacks of different commonly
applied heuristic solutions, drawing general design insights on the optimal
policy.
