BOLA: Near-optimal bitrate adaptation for online videos

@article{Spiteri2016BOLANB,
  title={BOLA: Near-optimal bitrate adaptation for online videos},
  author={Kevin Spiteri and Rahul Urgaonkar and Ramesh K. Sitaraman},
  journal={IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications},
  year={2016},
  pages={1-9}
}
Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes and enhancing the quality of the video shown to the user. A bitrate that is too high leads to frequent video freezes (i.e., rebuffering), while a bitrate that is too low leads to poor video quality. Video providers segment the video into short chunks and encode each chunk at multiple bitrates… 

BOLA: Near-Optimal Bitrate Adaptation for Online Videos

TLDR
It is proved that BOLA achieves a time-average utility that is within an additive term $O(1/V)$ of the optimal value, for a control parameter V related to the video buffer size, which is significantly higher than current state-of-the-art algorithms.

From theory to practice: improving bitrate adaptation in the DASH reference player

TLDR
Three novel adaptive bitrate algorithms are developed that provide higher QoE to the user in terms of higher bitrate, fewer rebuffers, and lesser bitrate oscillations and perform very well for live streams that require low latency, a challenging scenario for ABR algorithms.

FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming Over HTTP

TLDR
The proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to Bola, Festive, BBA, RB, FastMPC, and Pensieve algorithms.

HotDASH: Hotspot Aware Adaptive Video Streaming Using Deep Reinforcement Learning

TLDR
This work presents HotDASH, a system which enables opportune prefetching of user-preferred temporal video segments (called hotspots), and outperforms all baseline algorithms, with a 16.2% QoE improvement over the best-performing baseline, and achieves 14.31% better average bitrate due to its ability to prefetch opportunistically.

NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming

TLDR
Results showed that NeuSaver effectively reduces the power consumption of mobile devices when streaming video by an average of 16.14% and up to 23.12% while achieving high QoE.

Balancing Quality of Experience and Traffic Volume in Adaptive Bitrate Streaming

TLDR
BANQUET selects a suitable bitrate by estimating QoE and traffic volume that will be experienced by all the bitrate patterns for the next several chunks on the basis of future throughput and a buffer transition calculation, and the trace-based simulation showed that BANquET reduces traffic volume 18.3%–51.2% on average in the mobile environment and 1.2%"–38.3%" in the broadband environment.

Neural Adaptive Video Streaming with Pensieve by Hongzi Mao

TLDR
P Pensieve is developed, a system that generates ABR algorithms entirely using Reinforcement Learning (RL) and outperforms the best state-of-the-art scheme, with improvements in average QoE of 13.1%-25.0%.

Improving Quality of Experience by Adaptive Video Streaming with Super-Resolution

TLDR
A super-resolution based adaptive video streaming (SRAVS) framework is presented, which applies a Reinforcement Learning (RL) model for integrating the video super- resolution (VSR) technique with the video streaming strategy.

Layer-Assisted Adaptive Video Streaming

TLDR
This work proposes a solutions that employs both SVC and non-SVC video to improve user's QoE while avoiding the increased bandwidth overhead and HTTP signaling of SVC.

Vabis: Video Adaptation Bitrate System for Time-Critical Live Streaming

TLDR
This paper uses a frame-based approach to solve the I-frame misalignment problem and proposes a video adaptation bitrate system (Vabis) in units of the frame for time-critical live streaming to obtain the optimal quality of experience (QoE).
...

References

SHOWING 1-10 OF 28 REFERENCES

From theory to practice: improving bitrate adaptation in the DASH reference player

TLDR
Three novel adaptive bitrate algorithms are developed that provide higher QoE to the user in terms of higher bitrate, fewer rebuffers, and lesser bitrate oscillations and perform very well for live streams that require low latency, a challenging scenario for ABR algorithms.

Neural Adaptive Video Streaming with Pensieve

TLDR
P Pensieve is proposed, a system that generates ABR algorithms using reinforcement learning (RL), and outperforms the best state-of-the-art scheme, with improvements in average QoE of 12%--25%.

A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP

TLDR
A principled control-theoretic model is developed that can optimally combine throughput and buffer occupancy information to outperform traditional approaches in bitrate adaptation in client-side players and is presented as a novel model predictive control algorithm.

Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

TLDR
It is argued that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation, which is akin, but also orthogonal to the transport-layer TCP congestion control, and PANDA - a client-side rate adaptation algorithm for HAS is presented.

Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video Streaming With Festive

TLDR
A principled understanding of bit-rate adaptation is presented and a suite of techniques that can systematically guide the tradeoffs between stability, fairness, and efficiency are developed, which lead to a general framework for robust video adaptation.

Oboe: auto-tuning video ABR algorithms to network conditions

TLDR
Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions.

ELASTIC: A Client-Side Controller for Dynamic Adaptive Streaming over HTTP (DASH)

TLDR
This paper proposes ELASTIC (fEedback Linearization Adaptive STreamIng Controller), a client-side controller designed using feedback control theory that does not generate an on-off traffic pattern and is able to get the fair share when coexisting with TCP greedy flows.

Understanding the impact of video quality on user engagement

TLDR
This paper uses a unique dataset that spans different content types, including short video on demand, long VoD, and live content from popular video con- tent providers, to measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.

Dynamic adaptive streaming over HTTP --: standards and design principles

In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the

Video Stream Quality Impacts Viewer Behavior: Inferring Causality Using Quasi-Experimental Designs

TLDR
This work is the first to establish a causal relationship between video quality and viewer behavior, taking a step beyond purely correlational studies and using Quasi-Experimental Designs, a novel technique adapted from the medical and social sciences.