Experimental Evaluation of Large Scale WiFi Multicast Rate Control

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality

Streaming Video over HTTP with Consistent Quality

In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.Comment: Refined version submitted to ACM Multimedia Systems Conference (MMSys), 201

Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation

The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario