No abstract

No abstract

As the growth of mobile video traffic outpaces that of cellular network speed, industry is adopting HTTP-based adaptive video streaming technology which enables dynamic adaptation of video bit-rates to match changing network conditions. However, recent measurement studies have observed problems in fairness, stability, and efficiency of resource utilization when multiple adaptive video flows compete for bandwidth on a shared wired link. Through experiments and simulations, we confirm that such undesirable behavior manifests itself in cellular networks as well. To overcome these problems, we design an in-network resource management framework, AVIS, that schedules HTTP-based adaptive video flows on cellular networks. AVIS effectively manages the resources of a cellular base station across adaptive video flows. AVIS also provides a framework for mobile operators to achieve a desired balance between optimal resource allocation and user quality of experience. AVIS has three key differentiating features: (1) It optimally computes the bit-rate allocation for each user, (2) It includes a scheduler and per-flow shapers to enforce bit-rate stability of each flow and (3) It leverages the resource virtualization technique to separate resource management of adaptive video flows from regular video flows. We implement a prototype system of AVIS and evaluate it on both a WiMAX network testbed and a LTE system simulator to show its efficacy and scalability.

No abstract

With recent standardization and deployment of LTE eMBMS, cellular multicast is gaining traction as a method of efficiently using wireless spectrum to deliver large amounts of multimedia data to multiple cell sites. Cellular operators still seek methods of performing optimal resource allocation in eMBMS based on a complete understanding of the complex interactions among a number of mechanisms: the multicast coding scheme, the resources allocated to unicast users and their scheduling at the base stations, the resources allocated to a multicast group to satisfy the user experience of its members, and the number of groups and their membership, all of which we consider in this work. We determine the optimal allocation of wireless resources for users to maximize proportional fair utility. To handle the heterogeneity of user channel conditions, we efficiently and optimally partition multicast users into groups so that users with good signal strength do not suffer by being grouped together with users of poor signal strength. Numerical simulations are performed to compare our scheme to practical heuristics and state-of-the-art schemes. We demonstrate the tradeoff between improving unicast user rates and improving spectrum efficiency through multicast. Finally, we analyze the interaction between the globally fair solution and individual user's desire to maximize its rate. We show that even if the user deviates from the global solution in a number of scenarios, we can bound the number of selfish users that will choose to deviate. * The authors are in alphabetical order except for the 1 st author. 1 This work was done when Jiasi Chen and K.K. Ramakrishnan were at AT&T Labs.