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The emergence of bandwidth-intensive latency-critical traffic in 5G Networks,such as Virtual Reality, has motivated interest in wireless resource allocationproblems for flows with hard-deadlines. Attempting to solve this problem bringsabout two challenges: (i) The flow arrival and the channel state are not knownto the Base Station (BS) apriori, thus, the allocation decisions need to bemade online. (ii) Wireless resource allocation algorithms that attempt tomaximize a reward will likely be unfair, causing unacceptable service for someusers. We model the problem as an online convex optimization problem. Wepropose a primal-dual Deadline-Oblivious (DO) algorithm, and show it isapproximately 3.6-competitive. Furthermore, we show via simulations that ouralgorithm tracks the prescient offline solution very closely, significantlyoutperforming several existing algorithms. In the second part, we impose astochastic constraint on the allocation, requiring a guarantee that each userachieves a certain timely throughput (amount of traffic delivered within thedeadline over a period of time). We propose the Long-term Fair DeadlineOblivious (LFDO) algorithm for that setup. We combine the Lyapunov frameworkwith analysis of online algorithms, to show that LFDO retains thehigh-performance of DO, while satisfying the long-term stochastic constraints.

Is Deadline Oblivious Scheduling Efficient for Controlling Real-Time Traffic in Cellular Downlink Systems?