Sangeeta Ramakrishnan

SDN Based QoE Optimization for HTTP-Based Adaptive Video Streaming

Rapidly increasing volume of video streaming traffic creates a bandwidth crunch for service providers and network operators. Increased user expectations for higher quality video does not imply their willingness to pay higher monthly fees. Hence, more efficient bandwidth management schemes are needed to bridge the gap between growing demand from video traffic and existing network infrastructure. In this work, we present a novel bandwidth management solution for optimizing overall quality of experience (QoE) of multiple video streaming sessions. Instead of allocating bandwidth equally among competing flows, we propose to tailor the bandwidth allocation to both content complexity of requested video and playout buffer status of individual clients. We formulate the multi client bandwidth allocation problem within the convex optimization framework, which is flexible enough to accommodate a wide variety of video quality metrics. Further, we present a practical architecture based on software defined networking (SDN) with two components: video quality monitoring and video quality optimization. Testbed-based experiments confirm that with quality-optimized allocation the network can support up to 75% more users at the same level of quality-of-experience (QoE) than conventional equal-rate allocations.

Exploiting Video Quality Information With Lightweight Network Coordination for HTTP-Based Adaptive Video Streaming

This paper investigates how video-quality information can be exploited by HTTP-based adaptive streaming clients in their rate adaptation schemes. We also seek to answer the question: How much network coordination is required to achieve quality fairness across multiple competing clients? To that end, we envision a loosely coupled architecture where a lightweight centralized coordinator complements multiple federated quality-aware clients. Instead of actively programming network bandwidth allocation to each client, in our proposed architecture, the central coordinator simply collects quality and buffer level information from individual clients and publishes the aggregate quality statistics as reference. Such global reference statistics help to guide each federated client to self-tune its aggressiveness in its quality-aware adaptation scheme. Testbed-based evaluations show that such a lightweight approach is effective in reaping most of the performance gains previously achieved by a centralized joint optimization scheme. Compared to both state-of-the-art rate-based clients and independent quality-aware clients, our proposed federated quality-aware scheme can save up to

Optimizing Quality-of-Experience for HTTP-based Adaptive Video Streaming: An SDN-based Approach

\text{50}\%

Exploiting Video Quality Information in Rate Adaptation for HTTP-Based Video Streaming

of total bandwidth while maintaining comparable aggregate video quality across all clients.

System and method for multiple channel statistical re-multiplexing

In this work, the authors present a novel bandwidth management solution for optimizing overall quality of experience QoE of multiple video streaming sessions. Instead of allocating bandwidth equally among competing flows, they propose to tailor the bandwidth allocation to both content complexity of requested video and playout buffer status of individual clients. The authors formulate the multi-client bandwidth allocation problem within the convex optimization framework, which is flexible enough to accommodate a wide variety of video quality metrics. Further, the authors present a practical architecture based on software defined networking SDN with two components: video quality monitoring and video quality optimization. Testbed-based experiments confirm that with quality-optimized allocation the network can support up to 75% more users at the same level of quality-of-experience QoE than conventional equal-rate allocations.

Timing system for modular cable modem termination system

This paper explores how video quality information can be exploited by HTTP-based adaptive streaming (HAS) clients in their rate adaptation schemes. We first present an idealized combinatorial optimization formulation of the quality-aware rate adaptation problem and its corresponding solution. A few design principles are then introduced to arrive at a practical and robust rate adaptation scheme in the presence of real-world uncertainties (e.g., inaccuracy in bandwidth estimation, unforeseen changes in future video contents). Results from testbed-based experiments verify that by exploiting video quality information, our proposed quality-aware rate adaptation scheme significantly outperforms conventional rate-based clients in various contention-free and multi-client contention scenarios.