Ensuring high video quality of experience (QoE) on the broadcaster side is critical for interactive live streaming. However, measurements on multiple live streaming platforms show that they all suffer from broadcaster-side video quality degradation in the presence of transient bandwidth fluctuations. This paper presents Greedy Variable Bitrate (GVBR), a suite of solutions that optimizes the QoE through an approriate keyframe interval that trades cross-frame compression for lowered inter-frame interdependency, a simple-yet-efficient frame dropping strategy to prevent excessive frame drops, and a bitrate adaptation strategy customized for broadcasters who have shallow buffer. We compare GVBR with state-of-art algorithms in different network conditions, and find that GVBR can cut video interruption incidents by 90%, while achieving comparable bitrate.

Cooperative cache has become a promising technique to optimize the traffic by caching big data in networks. However, controlling distributed cache nodes to update cached contents synergistically is still challenging in designing cooperative cache systems. This paper proposes an SDN-based Cooperative Cache Network (SCCN) for ISP networks, aiming to minimize the content transmission latency while reducing the inter-ISP traffic. Based on the proposed increment recording mechanism, the SCCN Controller can timely capture the change of content popularity, and place the most popular contents on the appropriate SCCN Switches. We formulate the optimal content placement as a specific multi-commodity facility location problem and prove its NP-hardness. We propose a Relaxation Algorithm (RA) based on relaxation-rounding technique to solve the problem, which can achieve an approximation ratio of 1/2 in the worst case. To solve large scale problems for big data efficiently, we further design a Heuristic Algorithm (HA), which can find a near-optimal solution with three orders of magnitude speedup compared to RA. Specifically, HA can achieve a desirable tradeoff between the transmission delay and the Internet traffic. We implement a prototype based on Open vSwitch to demonstrate the feasibility of SCCN. Extensive trace-based simulation results show the effectiveness of SCCN under various network conditions.

Device to Device communication enables the deployment of mobile cloudlets in LTE-advanced networks. The distributed nature of mobile users and dynamic task arrivals makes it challenging to schedule tasks fairly among multiple devices. Leveraging the idea of software defined networking, we propose a software defined cooperative offloading model (SDCOM), where the SDCOM controller is deployed at the PDN gateway and schedules tasks in a centralized manner to save the energy of mobile devices and reduce the traffic on access links. We formulate the minimum-energy task scheduling problem as a 0-1 knapsack problem and prove its NP-hardness. To compute the optimal solution as a benchmark, we design the conditioned optimal algorithm based on the aggregated analysis of energy consumption. The greedy algorithm with a polynominal-time complexity is proposed to solve large-scale problems efficiently. To address the problem without predicting future information on task arrivals, we further design an online task scheduling algorithm (OTS). It can minimize the energy consumption arbitrarily close to the optimal solution by appropriately setting the tradeoff coefficient. Moreover, we extend OTS to design a proportional fair online task scheduling algorithm to achieve the fair energy consumption among mobile devices. Extensive trace-based simulations demonstrate the effectiveness of SDCOM for a variety of typical mobile devices and applications.